Agenda packet, first session — Santa Barbara City Council, March 12, 2024 (part 1 of 6)

CITY OF SANTA BARBARA

CITY COUNCIL
Randy Rowse
Mayor
Mike Jordan
Mayor Pro Tempore
Sarah J. Knecht
Interim City Administrator
Mike Jordan
Ordinance Committee Chair
Tava Ostrenger
Acting City Attorney
Alejandra Gutierrez
Finance Committee Chair
Eric Friedman
Meagan Harmon
Oscar Gutierrez
Kristen W. Sneddon
City Hall
735 Anacapa Street
http://www.SantaBarbaraCA.gov
MARCH 12, 2024, 2:00 PM
AGENDA
IN-PERSON PUBLIC COMMENT: At the beginning of each meeting of the City Council, Finance Committee, or Ordinance Committee, any member of the public may address the City Council concerning any item not on the Council's agenda. Any person wishing to make such address should first complete and deliver a "Request to Speak" form prior to the time that public comment is taken up by the City Council. Each speaker will be given a total of 3 minutes to address the Council. Pooling of time is not allowed during general public comment. The time allotted for general public comment at the beginning of the 2:00 p.m. session is 30 minutes.

Any member of the public who did not speak during the 2:00 p.m. session but who submitted a request to do so during the 2:00 p.m. 30 minute session may do so at the end of the meeting when the additional “Public Comment (If Necessary)” is announced. The City Council, upon majority vote, may decline to hear a speaker on the grounds that the subject matter is beyond their jurisdiction.

REMOTE PUBLIC COMMENT: Members of the public wishing to speak must “raise their hand” in the Zoom platform by selecting the virtual hand icon when their item is called. The keyboard shortcut for this is Alt+Y for Windows and Option+Y for Macs. City staff will activate the speaker’s microphone when the speaker’s name is called. The speaker will then need to unmute themselves. The keyboard shortcut for this is Alt+M for Windows or Command-Shift-A for Macs.

HOW TO REMOTELY OBSERVE AND/OR SPEAK LIVE AT A MEETING
 Web: https://santabarbaraca-gov.zoom.us/webinar/register/WN_JJS2kazqRyu2i83tshCkPw (Participation via Zoom)
 Telephone: Dial 1-669-900-6833, Enter Webinar ID: 894 2384 2846 (press *9 to raise/lower hand; press *6 to mute/unmute)  Online Streaming: Council meetings are streamed live at www.SantaBarbaraCA.gov/CAP  TV: Each regular City Council meeting is broadcast live in English and Spanish on City TV Channel 18 and rebroadcast in English on Wednesdays and Thursdays at 7:00 p.m. and Saturdays at 8:00 a.m., and in Spanish on Sundays at 3:00 p.m.

Each televised Council meeting is closed captioned for the hearing impaired. Check the City TV program guide at www.santabarbaraca.gov/citytv for rebroadcasts of Finance and Ordinance Committee meetings.

WRITTEN PUBLIC COMMENT: Public comments may also be submitted via email to [email protected] prior to the beginning of the Council Meeting. All public comments submitted via email will be provided to City Council and will become part of the public record.
(CONTINUED ON NEXT PAGE)
ORDER OF BUSINESS: Regular meetings of the Finance Committee and the Ordinance Committee begin at 12:00 p.m. The regular City Council meeting begins at 2:00 p.m. in the Council Chambers at City Hall.

REPORTS: Copies of the reports relating to agenda items are available for review at http://www.SantaBarbaraCA.gov/CAP.In accordance with state law requirements, this agenda generally contains only a brief general description of each item of business to be transacted or discussed at the meeting. Should you wish more detailed information regarding any particular agenda item, you are encouraged to obtain a copy of the Council Agenda Report (a "CAR") online at the City's website (http://www.SantaBarbaraCA.gov/CAP). Materials related to an item on this agenda submitted to the City Council after distribution of the agenda packet are posted to the City’s website as soon as reasonably feasible.

CONSENT CALENDAR: The Consent Calendar is comprised of items that will not usually require discussion by the City Council. A Consent Calendar item is open for discussion by the City Council upon request of a Councilmember, City staff, or member of the public. Items on the Consent Calendar may be approved by a single motion. Should you wish to comment on an item listed on the Consent Agenda, after turning in your "Request to Speak" form, you should come forward to speak or raise your hand in Zoom at the time the Council considers the Consent Calendar.

SPANISH INTERPRETATION: If you need interpretation of your communications to Council from Spanish into English, please contact the City Clerk’s Office at 564-5309 or by email at [email protected]. If possible, notification of at least 48 hours will usually enable the City to make arrangements.

INTERPRETACIÓN EN ESPAÑOL: Si necesita una interpretación del español al inglés, para sus comunicaciones al Consejo, comuníquese con la Oficina del Secretario Municipal al 564-5309, o por correo electrónico a [email protected]. Si es posible, la notificación de al menos 48 horas generalmente permitirá a la Ciudad hacer los arreglos.

AMERICANS WITH DISABILITIES ACT: If you need auxiliary aids or services or staff assistance to attend or participate in this meeting, please contact the City Administrator's Office at 564-5305 or by email at [email protected]. If possible, notification at least 48 hours prior to the meeting will usually enable the City to make reasonable arrangements. For those who need accessibility accommodation in using the “raise hand” function and/or registering to participate in the Zoom session, please contact the Clerk’s office by 5:00 p.m. the day before the meeting for assistance. Additionally, a speaker may email [email protected] by 5:00 p.m. the day before a meeting, stating which item they wish to speak on. Specialized services, such as sign language interpretation or documents in Braille, may require additional lead time to arrange. 03/12/2024 Santa Barbara City Council Final Agenda Page 1 MARCH 12, 2024 AGENDA
FINANCE COMMITTEE MEETING - 12:00 P.M. IN THE DAVID GEBHARD PUBLIC MEETING ROOM, 630 GARDEN STREET (120.03)
. Subject: Finance Committee Meeting Agenda (120.03)
Recommendation:

1. Subject: Restructure of Domestic Violence Solutions Loan on Property Located at 521 West Victoria Street (120.03)
Recommendation: That Finance Committee consider and recommend that City Council introduce and subsequently adopt, by reading of title only, an Ordinance of the Council of the City of Santa Barbara Approving a Restated Loan Agreement in the Amount of $430,000 with Domestic Violence Solutions Secured by a Restated Deed of Trust, and 90-Year Affordability Control Covenant Imposed on Real Property Located at 521 West Victoria Street, and Authorizing the Community Development Director to Execute Such Agreements, Subject to Approval as to Form by the City Attorney, as Necessary.

2. Subject: Establishing Fees for the Citywide Parklet Program (120.03) Recommendation: That Finance Committee consider and recommend that City Council adopt, by reading of title only, a Resolution of the Council of the City of Santa Barbara Establishing Fees for the Citywide Parklet Program.

REGULAR CITY COUNCIL MEETING - 2:00 P.M.

CALL TO ORDER

PLEDGE OF ALLEGIANCE

ROLL CALL

CEREMONIAL ITEMS
03/12/2024 Santa Barbara City Council Final Agenda Page 2

1. Subject: Arbor Week March 7-14, 2024 (120.04)
Recommendation:

CHANGES TO THE AGENDA

CONSENT CALENDAR

2. Subject: Proposed Municipal Code Amendments Pertaining to Safe Firearm Storage in the City of Santa Barbara [Ordinance Adoption] (570.08)
Recommendation: That Council adopt, by reading of title only, an Ordinance of the Council of the City of Santa Barbara Amending the Santa Barbara Municipal Code by Adding Chapter 9.35 Regarding Safe Storage of Firearms.

3. Subject: Corrected Headers in Table 30.20.030.B and Corrected Section Number 30.25.030 in Title 30 of the Santa Barbara Municipal Code Pertaining to the Average Unit-Size Density Incentive Program [Ordinance Adoption] (660.01)
Recommendation: That Council adopt, by reading of title only, an Ordinance of the Council of the City of Santa Barbara Amending the Santa Barbara Municipal Code to Correct Clerical Errors in the Headers in Table 30.20.030.B and Section Number 30.25.030 Pertaining to the Average Unit-Size Density Incentive Program.

4. Subject: Proposed Assignment of Lease Agreement No. 28,360 – Anchor Rose Lounge, Located Downstairs at 113 Harbor Way [Ordinance Adoption; Agreement] (570.03)
Recommendation: That Council adopt, by reading of title only, an Ordinance of the Council of the City of Santa Barbara Authorizing the Waterfront Director to Execute the Consent to Assignment of Lease Agreement No. 28,360, Formerly Known as Agreement No. 27,073, with Captain’s Cove LLC Transferring 51% of Ms. Amy Rose’s Capital Interest to Mr. Bryan Deminie for the Anchor Rose Lounge, Located Downstairs at 113 Harbor Way, Commencing Upon the Effective Date of the Enabling Ordinance.

5. Subject: Proposed Assignment of Lease Agreement No. 28,361 – Anchor Rose Restaurant, Located Upstairs at 113 Harbor Way [Ordinance Adoption; Agreement] (570.03)
03/12/2024 Santa Barbara City Council Final Agenda Page 3 Recommendation: That Council adopt, by reading of title only, an Ordinance of the Council of the City of Santa Barbara Authorizing the Waterfront Director to Execute the Consent to Assignment of Lease Agreement No. 28,361, Formerly Known as Agreement No. 23,328, with Captain’s Cove LLC Transferring 51% of Ms. Amy Rose’s Capital Interest to Mr. Bryan Deminie for the Anchor Rose Restaurant, Located Upstairs at 113 Harbor Way, Commencing Upon the Effective Date of the Enabling Ordinance.

6. Subject: Lease Amendment with Stearns Wharf Company, DBA Moby Dick Restaurant, Located at 220 Stearns Wharf [Ordinance Adoption; Agreement] (570.03)
Recommendation: That Council adopt, by reading of title only, an Ordinance of the Council of the City of Santa Barbara Approving and Authorizing the Waterfront Director to Execute the Amendment to Lease Agreement No. 15,309, Formerly Known as Agreement No. 10,356 with Stearns Wharf Company, DBA Moby Dick Restaurant, Deferring Three Months Base Rent with Lease Terms Ending December 31, 2030, Commencing Upon the Effective Date of the Enabling Ordinance.

7. Subject: Resolution to Authorize Airport Patrol to Join California Peace Officer Standards and Training [Resolution] (560.01)
Recommendation: That Council:

A. That Council authorize, through resolution, the adoption of California Resolution to Authorize Airport Patrol to join California Peace Officer Standards and Training’s minimum standards for the selection and training of peace officers at the Santa Barbara Airport Patrol; and
B. Adopt, by reading of title only, a Resolution of the Council of the City of Santa Barbara accepting the requirements of Sections 13510, 13512, and 13522 of the California Penal Code, relating to the recruitment and training of peace officers for the Airport Patrol.

8. Subject: Citywide Traffic Model Update: Adoption of the Travel Model Development Report, Traffic Impact Analysis, Traffic Analysis Guidelines, and Master Environmental Assessment Guidelines for Transportation Analysis [Resolution] (670.09)
Recommendation: That Council adopt, by reading of title only, a Resolution of the Council of the City of Santa Barbara Adopting the Travel Model 03/12/2024 Santa Barbara City Council Final Agenda Page 4 Development Report, Traffic Impact Analysis, Traffic Analysis Guidelines, and Master Environmental Assessment Guidelines for Transportation Analysis.

9. Subject: Authorization to Execute US Department of Agriculture Emergency Watershed Protection Grant for Coronel Path Slope Restoration Project [Resolution, Agreement] (530.01)
Recommendation: That Council:

A. Authorize the Public Works Director to execute the Natural Resources Conservation Service Cooperative Agreement between the City of Santa Barbara and the United States Department of Agriculture, Natural Resources Conservation Service; and
B. Adopt, by reading of title only, a Resolution of the Council of the City of Santa Barbara Amending Resolution No. 23-076, Adopting the Budget for Fiscal Year 2024, to Appropriate Emergency Watershed Protection Program Grant Funds and City Funds for the Coronel Path Repair Project.

10. Subject: Advisory Group Guidelines Update [Resolution] (140.02) Recommendation: That Council adopt, by reading of title only, a Resolution of the City of Santa Barbara Rescinding Resolution No. 13-006, and Adopting New Advisory Group Guidelines.

11. Subject: Cottage Hospital and One805 Grant Acceptance for Purchase of 30 Automated External Defibrillators (AED) and 30 Five-Year Safety and Maintenance Plans for Santa Barbara Police Department Emergency Response Use (520.02)
Recommendation: That Council approve and authorize the Chief of Police and the Santa Barbara Police Department to accept grant funding from Cottage Hospital and One805 for the purchase of 30 Automated External Defibrillators (AED) and the LIFELINKcentral AED Program Manager Safety and Maintenance Plan to accompany the AEDs.

CONSENT PUBLIC HEARING

12. Subject: Five-Year Measure A Local Program of Projects for Fiscal Years 2025 Through 2029 Public Hearing [Resolution] (670.08)
03/12/2024 Santa Barbara City Council Final Agenda Page 5 Recommendation: That Council adopt, by reading of title only, a Resolution of the Council of the City of Santa Barbara Adopting the Five-Year Measure A Local Program of Projects for Fiscal Years 2025 Through 2029.

PUBLIC COMMENT

COUNCILMEMBER COMMITTEE ASSIGNMENT REPORTS

CITY COUNCIL ADMINISTRATIVE AND ATTORNEY REPORTS
- AIRPORT DEPARTMENT

13. Subject: Santa Barbara Airport Master Plan Update (560.09) Recommendation: That Council receive a briefing on the Santa Barbara Airport Master Plan Update.
- PUBLIC WORKS DEPARTMENT

14. Subject: Establishing Fees for the Citywide Parklet Program [Resolution] (530.04)
Recommendation: That Council adopt, by reading of title only, a Resolution of the Council of the City of Santa Barbara Establishing Fees for the Citywide Parklet Program.
- CITY ADMINISTRATOR

15. Subject: Annual Report on Community Oversight of Law Enforcement and Fire and Police Commission Work Plan (520.01)
Recommendation: That Council receive the annual report on community oversight of law enforcement and work plan submitted on behalf of the Fire and Police Commission.

COUNCIL AND STAFF COMMUNICATIONS

COUNCILMEMBER ENGAGEMENTS
03/12/2024 Santa Barbara City Council Final Agenda Page 6

PUBLIC COMMENT (IF NECESSARY)

ADJOURNMENT

CITY OF SANTA BARBARA

FINANCE COMMITTEE MEETING

MEETING AGENDA
DATE: March 12, 2024 Alejandra Gutierrez, Chair
TIME: 12:00 PM Eric Friedman
PLACE: David Gebhard Public Meeting Room Meagan Harmon
630 Garden Street
Sarah Knecht Keith DeMartini
Interim City Administrator Finance Director
Members of the public may participate in person or via Zoom. Zoom instructions are below.  Web: (Participation via Zoom) Register in advance for this webinar:
https://santabarbaraca-gov.zoom.us/webinar/register/WN_TRT4cwrnQcG0RL_OtwThJg Webinar ID: 828 0394 3865
Passcode: 041173
After registering, you will receive a confirmation email containing information about joining the webinar.
 Telephone: Dial 1-669-900-6833, Enter Webinar ID: 828 0394 3865 (press *9 to raise/lower hand; press *6 to mute/unmute) REMOTE PUBLIC COMMENT: Members of the public wishing to speak must “raise their hand” in the Zoom platform by selecting the virtual hand icon when their item is called. The keyboard shortcut for this is Alt+Y for Windows and Option+Y for Macs. City staff will activate the speaker’s microphone when the speaker’s name is called. The speaker will then need to unmute themselves.

The keyboard shortcut for this is Alt+M for Windows or Command-Shift-A for Macs.

For those who need accessibility accommodation in using the “raise hand” function and/or registering to participate in the Zoom session, please contact the Finance Department by 5:00 p.m. the day before the meeting for assistance. Additionally, a speaker may email [email protected] by 5:00 p.m. the day before a meeting, stating the item on which they wish to speak.

Agenda Item No.

File Code No. 120.03
ITEMS FOR CONSIDERATION:

2. Subject: Establishing Fees for the Citywide Parklet Program (120.03) Recommendation: That Finance Committee recommend that City Council adopt, by reading of title only, a Resolution of the Council of the City of Santa Barbara Establishing Fees for the Citywide Parklet Program.

CITY OF SANTABARBARA

FINANCE COMMITTEE AGENDA REPORT
AGENDA DATE: March 12, 2024
TO: Finance Committee
FROM: Housing and Human Services Division, Community
Development Department
SUBJECT: Restructure of Domestic Violence Solutions Loan on Property Located at 521 West Victoria Street
RECOMMENDATION:

That Finance Committee consider and recommend that City Council introduce and subsequently adopt, by reading of title only, an Ordinance of the Council of the City of Santa Barbara Approving a Restated Loan Agreement in the Amount of $430,000 with Domestic Violence Solutions Secured by a Restated Deed of Trust, and 90-Year Affordability Control Covenant Imposed on Real Property Located at 521 West Victoria Street, and Authorizing the Community Development Director to Execute Such Agreements, Subject to Approval as to Form by the City Attorney, as Necessary.

BACKGROUND:

Domestic Violence Solutions of Santa Barbara County (DVS) provides a wide range of services to survivors of domestic violence from across Santa Barbara County, including emergency housing, transitional housing, permanent housing, counseling, job and life skill training, and case management. They have served the City of Santa Barbara since their founding in 1977 with facilities that include a 25-bed emergency shelter and a 16-unit apartment complex serving the needs of domestic violence survivors. The apartment complex is the subject property in need of a loan restructure.

DISCUSSION:

Property Description
The property consists of 16 one-bedroom apartment units, offices, and a clinical counseling space as well as 16 parking spaces, all in a secured, fenced campus. Fifteen of the 16 units are under contract with the City of Santa Barbara Housing Authority as Section 8 housing. These units are available to domestic violence survivors at Low-Income rents. One unit is a transitional housing unit available for six to eight months to a survivor Agenda Item No. 1
File Code No. 120.03
Finance Committee Agenda Report
Restructure of Domestic Violence Solutions Loan on Property Located at 521 West Victoria Street
March 12, 2024

Page 2 ends

and family who has completed their time in one of the emergency shelters but isn’t financially ready for permanent housing.

Loan History and Default
On September 23, 1993, the City’s Redevelopment Agency provided a loan totaling $430,000 to Domestic Violence Solutions (DVS) for acquisition of a 16-unit apartment building located at 521 West Victoria Street to be operated as an affordable transitional housing project for low-income victims of domestic violence (Project). The loan term was for 30 years, accruing simple interest at 4.5 percent annually, with residual receipt1 payments due any year ending with a positive net cash flow.

On September 30, 2008, an Amendment to the loan agreement was executed to forgive all of the accrued interest on the loan in the amount of $343,000 and set the interest rate to accrue at zero percent annually due to the Project not being able to realize any positive annual net cash flow and the inability to make any payments to the loan. The Redevelopment Agency determined at that time that “it is very unlikely that owner will ever have the ability to make any payments toward the interest on the Agency Loan.” The loan matured on October 1, 2023, and full payment was due. The loan balance remains at the original principal balance of $430,000, as no payments have been received over the history of the loan. On April 19, 2023, DVS submitted a formal request for the City’s consideration to forgive the entirety of the loan (Attachment 1); however, due to DVS partnering with the Housing Authority several years ago to convert 15 of the 16 units to Section 8 rental payments, the cash flow on the property is now sufficient for a repayment of the loan.

Proposed Restructured Loan
The loan will be restructured to allow an additional 15 years for repayment; however, the first five years will be deferred to support DVS’s efforts to acquire additional property (or properties) in the next three years. They have sufficient reserves now and are planning to expand their housing facilities from three to as many as six properties to expand their transitional and permanent housing options.

Repayments will be amortized to ensure full repayment at loan maturity in 2039. The total monthly payment will be zero for the first five years, and approximately $3,583 per month over the next 10 years. This amount includes the following considerations:
1 Residual receipts are annual revenues minus annual costs.

Finance Committee Agenda Report
Restructure of Domestic Violence Solutions Loan on Property Located at 521 West Victoria Street
March 12, 2024

Page 3 ends

 The original $430,000 principal will be rewritten at zero percent simple interest for 15 years.
 Payments will be deferred for the first five years and then commence with monthly payments from there forward for an additional 10 years at $3,583 per month and continuing until the loan is paid in full.

Long-Term Affordability
 In 1993, a 30-year City Affordability Control Covenant Imposed on Real Property (Covenant) was recorded on the property in exchange for the loan provided to DVS from the City’s Redevelopment Agency.
 In 2004, in exchange for a rehabilitation grant provided by the City, the Covenant was extended for an additional 30 years, expiring on September 15, 2053.  As a condition of the restructured loan, the City will record a new 90-year Covenant with the required loan documents stipulating that the property must remain affordable to Lower-Income households for 90 years (2114).  The Covenant will continue to restrict tenancies to Low-Income households whose total income does not exceed 80 percent of the Area Median Income (AMI).

Conclusion
Staff supports the proposed loan restructure request because it supports the City’s General Plan Housing Element, Goal 3 – Provide Housing Assistance, Policy 3.1, which states, “Partner with and support other agencies and nonprofit organizations in their efforts to provide shelter and services for people experiencing homelessness or those on the verge of homelessness.”
Staff requests that the Finance Committee recommend that City Council approve the loan restructure request for property located at 521 West Victoria Street to continue to provide much-needed affordable housing units for Low-Income victims of domestic violence households for an additional 90 years.

BUDGET/FINANCIAL INFORMATION:

Funding for the original loan provided in the amount of $430,000 was disbursed in 1993 from the City’s now-dissolved Redevelopment Agency, and no additional funding will be needed to restructure the original principal. Once the loan documents are finalized and recorded, there will be a five-year payment deferral period followed by 10 years of monthly payments of $3,583. During the repayment period, the monthly payments will add Finance Committee Agenda Report
Restructure of Domestic Violence Solutions Loan on Property Located at 521 West Victoria Street
March 12, 2024

Page 4 ends

approximately $43,000 in annual principal repayments to the City’s Affordable Housing Fund.

ATTACHMENTS: 1. Domestic Violence Solutions Request Letter Dated April 19, 2023

2. Restructured Loan Amortization Schedules

3. Ordinance Draft
PREPARED BY: Laura Dubbels, Housing and Human Services Manager SUBMITTED BY: Elias Isaacson, Community Development Director APPROVED BY: City Administrator’sOffice
Attachment 1

Loan amount 430,000
Loan term 10 years 0 months
Interest rate 0
Calculate Clear
Monthly Pay: $3,583.33
100%
0% Principal
Interest
Total of 120 monthly
payments $430,000.00
Total interest $0.00
Year
$0
$100K
$200K
$300K
$400K
0 5 10
Balance
Interest
Payment
Annual Schedule Monthly Schedule
Year Interest Principal Ending
Balance
1 $0.00 $43,000.00 $387,000.00
2 $0.00 $43,000.00 $344,000.00
3 $0.00 $43,000.00 $301,000.00
4 $0.00 $43,000.00 $258,000.00
5 $0.00 $43,000.00 $215,000.00
6 $0.00 $43,000.00 $172,000.00
7 $0.00 $43,000.00 $129,000.00
8 $0.00 $43,000.00 $86,000.00
9 $0.00 $43,000.00 $43,000.00
10 $0.00 $43,000.00 $0.00
Amortization Calculator
Amortization schedule
by Calculator.net
Optional: make extra payments
Attachment 2
ATTACHMENT 3
ORDINANCE NO._____________

AN ORDINANCE OF THE COUNCIL OF THE CITY OF

SANTA BARBARA APPROVING A RESTATED LOAN
AGREEMENT IN THE AMOUNT OF $430,000 WITH

DOMESTIC VIOLENCE SOLUTIONS SECURED BY A

RESTATED DEED OF TRUST AND A NINETY-YEAR

AFFORDABILITY CONTROL COVENANT IMPOSED ON
REAL PROPERTY LOCATED AT 521 WEST VICTORIA

STREET, AND AUTHORIZING THE COMMUNITY

DEVELOPMENT DIRECTOR TO EXECUTE SUCH

AGREEMENTS, SUBJECT TO APPROVAL AS TO FORM

BY THE CITY ATTORNEY, AS NECESSARY
WHEREAS, DOMESTIC VIOLENCE SOLUTIONS ("Owner'') owns real property commonly known as 521 West Victoria Street in the City of Santa Barbara ("Property); and
WHEREAS, by minute action on April 9, 2024 the City Council introduced an ordinance to approve a 30-year loan restatement in the amount of Four Hundred Thirty Thousand Dollars ($430,000), originally provided by the City’s Redevelopment Agency ("City Loan"), secured by a deed of trust and a ninety-year affordability control covenant; and
WHEREAS, in exchange for the City Loan, Owner and City agree to record an Affordability Control Covenant imposed on Real Property restricting the rental of the Property to Transitional Housing ("Covenant") for a total of ninety (90) years to comply with the City's Affordable Housing Policies and Procedures; and WHEREAS, on April 16, 2024, the City Council, adopted by reading of title only an ordinance approving the City Loan, Deed of Trust and Covenant; and WHEREAS, the City Loan is secured by the Deed of Trust recorded in the office of the County Recorder on __________, 2024, as Instrument No.________________ of ATTACHMENT 3
official records for the purpose of securing Owner's obligations under the City Loan; and WHEREAS, Owner agrees to comply with Covenant recorded in the office of the County Recorder on , 2024, as Instrument No.
of official records.

THE COUNCIL OF THE CITY OF SANTA BARBARA DOES ORDAIN AS FOLLOWS:

In accordance with the provisions of Section 521 of the Charter of the City of Santa Barbara, an Ordinance of the Council of the City of Santa Barbara approving the City Loan, Deed of Trust and Covenant and authorizing approval by resolution of subsequent minor amendments to the agreements that do not extend their term, is hereby approved.

CITY OF SANTA BARBARA

FINANCE COMMITTEE AGENDA REPORT
AGENDA DATE: March 12, 2024
TO: Mayor and Councilmembers
FROM: Downtown Team Division, Public Works Department
SUBJECT: Establishing Fees for the Citywide Parklet Program RECOMMENDATION:

That Finance Committee consider and recommend that City Council adopt, by reading of title only, a Resolution of the Council of the City of Santa Barbara Establishing Fees for the Citywide Parklet Program.

DISCUSSION:

In response to the COVID-19 pandemic, the City of Santa Barbara (City) adopted successive ordinances to relax permitting requirements and allow businesses to expand operations into the Public Right of Way (ROW). The authorization adopted pursuant to Ordinance No. 6055, the Economic Recovery Extension and Transition Ordinance, expired on December 31, 2023.

On December 12, 2023, the Council adopted Ordinance 6134, adding Chapter 9.180 to the Municipal Code to authorize and regulate parklets citywide. The Council further directed the City Attorney to prepare a resolution establishing fees to process and license parklets.

Administration and License Issuance Process
The Public Works Director is responsible for the administration and enforcement of the new chapter, including issuing parklet licenses (Section 9.180.080.). The process for obtaining a license is specified in Section 9.180.110. To obtain a parklet license, an owner must first request a pre-application review by the Public Works Department. The purpose of the review is to advise the owner of all the applicable requirements and provide preliminary advice on whether a parklet is feasible at the proposed location.

Following preliminary review, the owner may file an application for a license with the Public Works Department. If the Public Works Department determines that the parklet meets all of the location and other mandatory requirements, then the application will be referred to the Community Development Department for design review. Further, the applicant will be advised of the need to apply for a Coastal Development Permit (CDP) Agenda Item No. 2
File Code No. 120.03
Finance Committee Agenda Report
Establishing Fees for the Citywide Parklet Program
March 12, 2024

Page 2 ends

when a parklet is proposed in the Coastal Zone (Section 9.180.130). This early review by the Public Works Department will ensure that only those parklets that meet the location and other mandatory requirements will be processed by Community Development.

Following design review, and coastal permitting if required, the application will be returned to the Public Works Department for final issuance of a parklet license.

A parklet license will take the form of an agreement approved by the City Attorney. It will include all terms and conditions for construction, operation, and maintenance of the parklet. A license will have a duration of up to five years as determined by the Public Works Director and is renewable. A licensee must pay an annual license fee, similar to rent, in an amount established by a City Council resolution (Sections 9.180.110 D,

9.180.150, 9.180.060.). There is no vested right to a parklet, and a parklet license does not create any vested right. Parklets are considered temporary use of public property (Section 9.180.170.).

Processing and Licensing Fees
Staff recommends the following fees for the processing of parklet applications and the issuance of parklet licenses. Recommended fees were determined based on an estimate of staff time required to process the application and a review of existing fees for similar City programs. Standard planning and design review fees will also be required.

Public Works Parklet Licensing Fees
Fee Name Applies To Amount
Preliminary Application Fee All Parklets $514
Parklet License Application Fee All Parklets $2,500
Annual Parklet License Fee All Parklets $24/sq ft/year
Community Development Fees
Fee Name Applies To Amount
CDP (Reduced) Coastal Zone $5,535
Design Review Fee* HLC/ABR $357-$693
Environmental Review Fee* All Parklets $435
Mailing Fee* All Parklets $199 x 3
Fire Code Compliance* All Parklets $263
*Existing Fee
Finance Committee Agenda Report
Establishing Fees for the Citywide Parklet Program
March 12, 2024

Page 3 ends

 Preliminary Application Fee: Staff recommends a fee of $514, which mirrors Community Development’s Preliminary Application Fee for Outdoor Dining on Private Property.
 Parklet License Application Fee: Staff recommends a Parklet License Application Fee of $2,500 to account for the staff time and resources required to review proposed parklet plans for compliance with engineering and traffic safety requirements.
 Parklet License Fee: Staff recommends an annual license fee equal to that of the Outdoor Business Facility License program of $2 per square foot per month. Fees are to be paid on an annual basis of $24 per square foot per year.  Coastal Development Permit: Based on an estimate of staff review time, staff concluded that these projects would take significantly less time than the standard PLN/CDP review, given the anticipated similarities between outdoor dining proposals and limited coastal issues. Staff is proposing a reduced CDP fee of $5,535 (currently, the CDP fee for this type of Program would be $15,666).

However, additional standard fees for environmental review, mailed noticing, Encroachment Permit, and design review, etc., will also be required.

BUDGET/FINANCIAL INFORMATION:

Council approved a reduced CDP fee of $5,535 for outdoor dining on private property at the October 3, 2023, meeting. This new CDP Outdoor Dining on Private Property fee of $5,535 was calculated to recover the full cost of services provided by the Community Development Department. In discussion with Community Development staff, it was determined that this fee would be appropriate for parklets in the public ROW. As of the Pre-Application deadline of February 1, 2024, 17 businesses submitted preliminary applications, nine of which are in the Coastal Development Zone. Any additional salary costs incurred as a result of the new and increased workload for Community Development staff are anticipated to be fully offset by revenue collected from the proposed CDP fee, in line with the City’s cost recovery and revenue policy. Additionally, the $514 preapplication fee and $2,500 license application fee were developed to cover costs incurred by Public Works during the review and issuance process. The proposed $24 per square foot annual fee would cover use of the public ROW for private business activity as well as ongoing staff costs to implement and manage the Program.

ENVIRONMENTAL REVIEW:

At its meeting on October 17, 2023, Council determined that the Citywide Parklet Program is categorically exempt from California Environmental Quality Act (CEQA) under CEQA Guidelines Sections 15301 and 15303 and directed the filing of a notice of exemption.

Finance Committee Agenda Report
Establishing Fees for the Citywide Parklet Program
March 12, 2024

Page 4 ends

PREPARED BY: Sarah Clark, Downtown Team Manager/MS/nv
SUBMITTED BY: Clifford M. Maurer, P.E., Public Works Director APPROVED BY: City Administrator’s Office
Agenda Item No. 1
File Code No. 120.04
1 ORDINANCE NO. _____

AN ORDINANCE OF THE COUNCIL OF THE CITY OF

SANTA BARBARA AMENDING THE SANTA BARBARA
MUNICIPAL CODE BY ADDING CHAPTER 9.35

REGARDING SAFE STORAGE OF FIREARMS

THE CITY COUNCIL OF THE CITY OF SANTA BARBARA DOES ORDAIN AS FOLLOWS:

SECTION 1. Title 9 of the Santa Barbara Municipal Code is amended by adding Chapter 9.35 which reads as follows:

9.35.010 Purpose and Intent.

Gun violence, mass shootings, and firearm related injuries among young people have been rising in the United States. Studies suggest that more than half of Americans have experienced gun violence or know someone close to them who has experienced gun violence. Santa Barbara is no different. In 2020, Santa Barbara Unified School District reported an increased number of weapon related incidents on school campuses.

Unsecured firearms at home have been shown to be correlated with a higher risk of both unintentional and intentional injury by firearm, especially among youth. Safely storing firearms in residences and vehicles will protect children and teens from accessing weapons and prevent injuries at home as well as protect public health and safety.

Recent growth of gun violence in the U.S. has disproportionately affected youth.

In 2023, the Santa Barbara Youth Council named gun violence one of its top issues for youth. After hearing young people’s concerns about gun violence in the community and at school at the Santa Barbara Youth Council: Youth Speak Out, the Youth Council voted to move forward with drafting a safe storage of firearms ordinance and proposing it to the Santa Barbara City Council.

This Chapter is adopted by City Council upon recommendation of the Santa Barbara Youth Council to further the following:

A. The protection of public health, safety, and general welfare.

B. A reduction in the risk of self-inflicted firearm related injuries and unintentional firearm related injuries among children and teens.

Agenda Item No. 2
File Code No. 570.08
2 9.35.020 Definitions.

The following definitions apply to the interpretation of this Chapter:

A. FIREARM. A firearm as defined in California Penal Code Section 16520, as amended from time to time. The term “firearm” does not include an antique permanently disabled firearm.

B. LOCKED CONTAINER. A locked container that is either: (1) a locked container, as defined in California Penal Code, Section 16850, and listed on the California Department of Justice Bureau of Firearms’ roster of approved firearm safety devices; or (2) approved in writing by the California Department of Justice Bureau of Firearms as having the equivalent effect to other approved devices. For purposes of this Chapter, a locked container does not include a bag or other container made of fabric or other penetrable material, such as a regular purse, backpack, or gym bag.

C. RESIDENCE. Any structure intended or used for human habitation, including but not limited to, houses, condominiums, apartments, rooms, accessory dwelling units, motels, hotels, vacation rentals, recreational vehicles, and other vehicles where human habitation occurs. “Residence” also includes the curtilage of the residence.

D. TRIGGER LOCK. A trigger lock that is either:

1. Listed on the California Department of Justice Bureau of Firearms' roster of approved firearms safety devices and that is identified as appropriate for that firearm by reference to either the manufacturer and model of the firearm or to the physical characteristics of the firearm that match those listed on the roster for use with the device under Penal Code Section 23635.

2. Approved in writing by the California Department of Justice Bureau of Firearms as having equivalent effect to other approved devices and is the appropriate make and model for the firearm the lock will be used on.

E. TRUNK. A fully enclosed compartment that is separate from the main cabin of the vehicle.

F. UNATTENDED VEHICLE. A vehicle that has no person aged 18 or over present in the vehicle.

G. VEHICLE. As defined by California Vehicle Code Section 415, any device that can propel, move, draw people or property on a highway.

H. PEACE OFFICER. As defined and codified under Title 3, Chapter 4.5., Sections 830 through 832.18 of the California Penal Code. 3 9.35.030 Safe Storage of Firearms in a Residence.

A. It is unlawful for a person to keep a firearm in any residence unless the firearm is stored in a locked container and is disabled with a trigger lock, except:

1. When the firearm is carried upon the person who owns the firearm.

2. When cleaning or maintaining the firearm.

3. When using the firearm for lawful self-defense.

B. This Section is intended to apply to all residences within the city of Santa Barbara. However, nothing in this Section shall apply to peace officers.

9.35.040 Safe Storage of Firearms in a Vehicle.

A. It is unlawful for a person to leave a firearm in an unattended vehicle unless the firearm is stored:

1. In a locked container in the trunk of the vehicle, disabled by a trigger lock.

2. For vehicles with no trunk, in a locked container, disabled by a trigger lock, and out of sight from the public.

B. It is unlawful for a person to leave a firearm in an unattended vehicle overnight.

C. This Section is not intended to conflict with any applicable state or federal law.

D. This Section is intended to apply to all vehicles within the city of Santa Barbara. However, nothing in this Section shall apply to peace officers or to federal officers or military personnel who are acting within the scope of their authority and discharging their official duties.

9.35.050 Reporting Theft or Loss to the Santa Barbara Police Department.

Nothing in this ordinance affects a person’s obligations under California Penal Code Section 25250 to report that a firearm has been lost or stolen to local authorities within 5 days at the time they knew or reasonably should have known that the firearm had been lost or stolen.

9.35.060 Penalty.

Any person who violates any mandatory provision of this Chapter shall be guilty of an offense punishable in accordance with Chapter 1.28 of this Code. 1 ORDINANCE NO. __________

AN ORDINANCE OF THE COUNCIL OF THE CITY

OF SANTA BARBARA AMENDING THE SANTA

BARBARA MUNICIPAL CODE TO CORRECT

CLERICAL ERRORS IN THE HEADERS IN TABLE

30.20.030.B AND SECTION NUMBER 30.25.030

PERTAINING TO THE AVERAGE UNIT-SIZE

DENSITY INCENTIVE PROGRAM

THE CITY COUNCIL OF THE CITY OF SANTA BARBARA DOES ORDAIN AS FOLLOWS:

SECTION 1. Section 30.20.030 of Chapter 30.20 of Title 30 of the Santa Barbara Municipal Code is amended to read as follows:

30.20.030 Development Standards.

Tables 30.20.030A and 30.20.030B prescribe the development standards for Residential Zones. Section numbers refer to other sections of this title, while individual letters refer to subsections that directly follow the tables. Additional regulations that apply throughout the City are located in Division III, Citywide Regulations.

TABLE 30.20.030.A: DEVLOPMENT STANDARDS–RESIDENTIAL SINGLE UNIT ZONES
Zone RS-1A RS-25 RS-15 RS-10 RS-7.5 RS-6
Lot Size and Street Frontage
Minimum Net Lot Area for
Newly Created Lots (sq. ft.
unless noted)
See also Chapter 27.60, Parcel Maps for Urban Lot
Splits
Average Slope less than 10%,
and all lots with frontage on
the Pacific Ocean regardless
of slope
1 acre 25,000 15,000 10,000 7,500 6,000
Average Slope 10% to 20% 1.5
acre
37,500 22,500 15,000 11,250 9,000
Average Slope over 20% to
30% 2 acres 50,000 30,000 20,000 15,000 12,000
Average Slope over 30% 3 acres 75,000 45,000 30,000 22,500 18,000 100 100 90 75 60 60 Minimum Public Street
Frontage (ft.) See also §30.140.180, Street Frontage and Access; and §30.140.120, Location of Lot Lines
Maximum Base Residential Density
Agenda Item No. 3
File Code No. 660.01
2 Maximum Density (per lot) 1 unit 1 unit 1 unit 1 unit 1 unit 1 unit Additional Residential Density Allowances
All lots, in compliance with the
applicable section
See §30.185.050, Additional Residential Unit
See §30.185.040, Accessory Dwelling Units
See Chapter 30.145, Affordable Housing and Density
Bonus and Development Incentives
Maximum Floor Area
Maximum Floor Area
(Floor to Lot Area Ratio) (sq.
ft.)
Applicable only to lots developed, or proposed to be
developed, with a building with two or more stories or
17 feet or more in height.

Less than 4,000 sq. ft. Net Lot
Area
2,200. See also A, Maximum Floor Area (Floor to Lot
Area Ratio)
4,000 to 9,999 sq. ft. Net Lot
Area
1,200 + (0.25 multiplied by the net lot area) = Maximum Floor Area. See also (A), Maximum Floor Area (Floor to
Lot Area Ratio)
10,000 to 14,999 sq. ft. Net
Lot Area
2,500 + (0.125 multiplied by the net lot area) =
Maximum Floor Area. See also A, Maximum Floor Area
(Floor to Lot Area Ratio)
15,000 and more sq. ft. Net
Lot Area Not Applicable
Structure Form and Location
Minimum Residential Unit Size
Studio: 220 square feet;
All other units: 400 square feet
See §30.140.150, Residential Unit
Maximum Height (ft.) 30, except as further limited in accordance with §30.140.170, Solar Access Height Limitations
Minimum Setbacks (ft.),
Residential Structures
See also §30.140.090, Encroachments into Setbacks
and Open Yards
Front 35 30 30 25 20
Portions of
structures 15
feet or less in
height: 15;
Portions of
structures
more than 15
feet in height:
20;
3 Street facing
covered
parking: 20
See also B, Setback Reduction for Sloping Lots
Interior 15 10 10 8 6 5
Minimum Setbacks (ft.),
Nonresidential Structures
See also §30.140.090, Encroachments into Setbacks
and Open Yards
Front Same as for Residential Structures
Interior (Conversion and
Alterations)
Conversions of existing residential structures to
nonresidential structures, or alterations of existing
structures that contain nonresidential uses, are subject to the setback requirements for residential structures. 30 20 20 12 12 10
Interior (New or Additions) Exception: Community Gardens are subject to the setback requirements for residential structures.

Minimum Setbacks, MixedUse
Portions of structures that contain residential uses are subject to the setback for residential structures, and
portions of structures used for nonresidential uses are subject to the setback requirement for nonresidential
structures.

Portions of structures used by both the residential and nonresidential uses are subject to the setback
requirement for nonresidential structures.

Minimum Setbacks,
Uncovered Parking
See §30.175.060, Location of Required Automobile and
Bicycle Parking
Materials
Roofing and siding materials shall be nonreflective.

Shiny, mirror like, or glossy metallic finishes are
prohibited.

Open Yard
Open Yards See §30.140.140, Open Yards
4 TABLE 30.20.030.B: DEVELOPMENT STANDARDS–TWO-UNIT AND MULTI-UNIT ZONES
Zone R-2 R-M R-MH
Lot Size and Street Frontage
Minimum Net Lot Area for
Newly Created Lots (sq. ft.)
Average Slope less than 10% 7,000
Average Slope 10% to 20% 10,500
Average Slope over 20% to
30% 14,000
Average Slope over 30% 21,000
14,000 14,000
60 60 60 Minimum Public Street
Frontage (ft.) See §30.140.180, Street Frontage and Access; and §30.140.120, Location of Lot Lines
Maximum Base Residential Density
Less than 5,000 sq. ft. Net Lot
Area 1 unit 1 unit
5,000 to 5,999 sq. ft. Net Lot
Area 1 unit 2 units
6,000 to 6,999 sq. ft. Net Lot
Area
2 units if
average slope
less than
10%, 1 unit
otherwise
2 units
7,000 and more sq. ft. Net Lot
Area
Average Slope less than 10%
1 unit/3,500 sq.
ft. of net lot
area
Average Slope 10% to 20%
1 unit/5,250 sq.
ft. of net lot
area
Average Slope over 20% to
30%
1 unit/7,000 sq.
ft. of net lot
area
Average Slope over 30%
1 unit/10,500
sq. ft. of net lot
area
3 units, or 1 unit/3,500 sq. ft. of net lot
area, whichever is greater
Additional Residential Density Allowances
All lots, in compliance with the
applicable section
See §30.185.040, Accessory Dwelling Units
5 See Chapter 30.150, Average Unit-Size Density
Incentive Program
See Chapter 30.145, Affordable Housing and Density
Bonus and Development Incentives
Structure Form and Location
Minimum Residential Unit
Size See §30.140.150, Residential Unit
30 45 45
Maximum Height (ft.) Except as further limited in accordance with §30.140.170, Solar Access Height Limitations
Minimum Setbacks (ft.),
Residential Structures
See also §30.140.090, Encroachments into Setbacks
and Open Yards
See Chapter 30.150, Average Unit-Size Density
Incentive Program
1st and 2nd Stories: 10
Portions of structures above 2nd story:
20 Front
Portions of
structures 15
feet or less in
height: 15;
Portions of
structures more
than 15 feet in
height: 20;
Street facing
covered
parking: 20
See also (B),
Setback
Reduction for
Sloping Lots
Covered parking: 10
Covered parking, street-facing: 20
1st and 2nd Stories: 6
Portions of structures above 2nd story:
10 Interior
Covered
parking: 3
Other
structures: 6
Covered parking:
● Single-Unit Residential and Two-Unit
Residential: 3
● Multi-Unit Residential: 6 except as
provided in C, Interior Setback
Reduction for Covered Parking on Lots
6 Additional Residential Zone Development Regulations
A. Maximum Floor Area (Floor to Lot Area Ratio).
55 Feet or Less and D, Interior Setback
for Covered Parking Opposite the
Primary Front Lot Line
Minimum Setbacks (ft.),
Nonresidential Structures
See also §30.140.090, Encroachments into Setbacks
and Open Yards
Front Same as for Residential Structures
Interior (Conversions and
Alterations)
Conversions of existing residential structures to
nonresidential structures, or alterations of existing
structures that contain nonresidential uses, are subject to the setback requirements for residential structures.

Interior (New or Additions) 12
New nonresidential structures or
additions to existing nonresidential
structures are subject to the following
interior setback:
● 1st and 2nd Stories: 12
● Portions of structures above
2 nd story: 20
● Exception: Neighborhood Markets
and Community Gardens are subject to
the setback requirements for residential
structures.

Portions of structures used by both the residential and nonresidential uses are subject to the setback
requirement for nonresidential structures.

Minimum Setbacks,
Uncovered Parking
See §30.175.060, Location of Required Automobile and
Bicycle Parking
Open Yard
Open Yards
See §30.140.140, Open Yards
See Chapter 30.150, Average Unit-Size Density
Incentive Program
7 1. Floor Area, Precluded Development-RS Zones. No application for a Building Permit may be approved for a project in an RS Zone that will: (1) result in an increase of the floor area on the lot; (2) change the location of any floor area on the second or higher story of any structure on the lot; or (3) increase the height of any portion of a structure on the lot to 17 feet or higher if any of the following will occur:
a. The floor area will exceed the allowable maximum floor area for the lot; or
b. The floor area will exceed 85% of the allowable maximum floor area and any of the following apply:
i. The average slope of the lot or the building site is 30% or greater,
ii. The height of any structure on the lot is more than 25 feet, or iii. The lot is located in the Hillside Design District and 500 or more cubic yards of grading is proposed to occur outside the footprint of the main or accessory buildings. Soil located within five feet of an exterior wall of a main or accessory building that is excavated and re compacted shall not be included in the calculation of the volume of grading outside the building footprint.

2. Measuring Floor Area Pursuant to this Section. In determining floor area pursuant to this section see Section 30.15.070, Measuring Floor Area, and the following:
a. Below Grade Excluded. On any floor which is partly below and partly above grade, such as a basement, cellar, or understory, the total floor area of that floor may be excluded from the floor to lot area ratio (FAR) if no more than a cumulative total width of 12 feet per elevation has a distance from finished grade to ceiling greater than four feet.
b. Partially Below Grade Reduced. On any floor which is partly below and partly above grade, such as a basement, cellar or understory, the total floor area of that floor may be reduced by 50% from the floor to lot area ratio (FAR) if more than 12 feet, but less than one half the entire length of the perimeter has a distance from grade to ceiling greater than four feet. c. All Other Floor Area Included. If more than one half the entire length of the perimeter of any floor has a distance from grade to ceiling greater than four feet, it is included in the floor to lot area ratio (FAR). 8 d. Accessory Dwelling Unit and Junior Accessory Dwelling Unit Included. Floor area within a portion of a structure designed and permitted as an accessory dwelling unit or junior accessory dwelling unit is included.

FIGURE 30.20.030.A.2: MEASURING FLOOR AREA
B. Setback Reduction for Sloping Lots.

1. Residential Single Unit Zones. In Residential Single Unit zones, where the average natural slope of the area within 50 feet of the front lot line is more than 20%, the required front setback for all stories is reduced by five feet.

2. R-2 Zone. In the R-2 Zone, where the average natural slope of the area within 50 feet of the front lot line is more than 20%, the required front setback for all stories is reduced to 10 feet.

C. Interior Setback Reduction for Covered Parking on Lots 55 Feet or Less. The required interior setback for covered parking on lots in the R-M or R-MH zone less than 55 feet in width, measured at the location of the covered parking structure, and developed with multi-unit residential may be reduced to three feet by the appropriate Design Review body provided the covered parking opening does not face a street or alley and the interior depth of the covered parking structure does not exceed 20 feet.

D. Interior Setback Reduction for Covered Parking Opposite the Primary Front Lot Line. The required interior setback for covered parking on lots in the R-M or RMH zone developed with multi-unit residential may be reduced to three feet on the lot line opposite the primary front lot line. In the event of two or more front lot lines, the setback of only one opposite lot line may be reduced to three feet.

FIGURE 30.20.030.C AND D: INTERIOR SETBACK REDUCTION FOR COVERED

PARKING
9 SECTION 2. Section 30.25.030 of Chapter 30.25 of Title 30 of the Santa Barbara Municipal Code is amended to read as follows:

30.25.030 Development Standards.

Table 30.25.030 prescribes the development standards for Commercial and Office Zones.

Section numbers refer to other sections of this Title, while individual letters refer to subsections that directly follow the table. Additional regulations that apply throughout the City are located in Division III, Citywide Regulations.

TABLE 30.25.030: DEVELOPMENT STANDARDS–COMMERCIAL AND OFFICE ZONES
Zone O-R O-M C-R C-G
Lot Size and Street Frontage
Minimum Net Lot Area for
Newly Created Lots (sq. ft.)
None; except 3,500 sq. ft. of net lot area is required for lots that include residential uses
Minimum Public Street
Frontage (ft.)
None, See §30.140.180, Street Frontage and Access; and
§30.140.120, Location of Lot Lines
Maximum Base Residential Density
Less than 5,000 sq. ft. Net
Lot Area 1 unit
10 TABLE 30.25.030: DEVELOPMENT STANDARDS–COMMERCIAL AND OFFICE ZONES
Zone O-R O-M C-R C-G
5,000 to 6,999 sq. ft. Net
Lot Area 2 units
7,000 and more sq. ft. Net
Lot Area
3 units, or 1 unit/3,500 sq. ft. of net lot area, whichever is greater
Additional Residential Density Allowances
All lots, in compliance with
the applicable section
See §30.185.040, Accessory Dwelling Units
See Chapter 30.150, Average Unit-Size Density Incentive Program
See Chapter 30.145, Affordable Housing and Density
Bonus and Development Incentives
Structure Form and Location
Minimum Residential Unit
Size
Studio: 220 square feet;
All other units: 400 square feet
See §30.140.150, Residential Unit
Maximum Height (ft.) 45 45 45
45;
60 for Community
Benefit Project or
Community Benefit
Housing Project
(§30.140.100.B)
See also A,
Theater Additions
Additional Height
Limitations Adjacent to all
Residential Zones
Height is further limited in accordance with §30.140.170, Solar Access Height Limitations
Minimum Setbacks (ft.)
See also §30.140.090, Encroachments into Setbacks and
Open Yards
See Chapter 30.150, Average Unit-Size Density Incentive Program
Front
Residential only development: Same
as R-M Zone
Nonresidential and mixed-use
development:

Residential only
development:

Same as R-M Zone
11 TABLE 30.25.030: DEVELOPMENT STANDARDS–COMMERCIAL AND OFFICE ZONES
Zone O-R O-M C-R C-G
● 1st and 2nd Stories: 10
● Portions of structure above
2 nd story: 20
Nonresidential and
mixed-use
development: 0
Interior, Adjacent to a
Nonresidential Zone
Residential only
development: Same as
R-M Zone
Nonresidential and
mixed-use development:
● 1st and 2nd Stories: 6
● Portions of structure
above 2nd story: 10
Residential only
development: Same as R-M
Zone
Nonresidential and mixed-use
development: 0
Interior, Adjacent to a
Residential Zone
Residential structures, and the residential portion of any mixed-use development: Same as R-M Zone
Nonresidential structures, and the nonresidential portion of any structure in a mixed-use development, 30 feet or less in height: 15
Nonresidential structures, and the nonresidential portion of any structure in a mixed-use development, more than
30 feet in height: 20
Portions of structures used by both the residential and nonresidential uses are subject to the setback
requirement for nonresidential structures.

Uncovered Parking See §30.175.060, Location of Required Automobile and Bicycle Parking
Open Yard
Open Yards
Any lot developed with residential uses shall provide open yard pursuant to §30.140.140, Open Yards.

See Chapter 30.150, Average Unit-Size Density Incentive Program
Additional Commercial and Office Zone Development Regulations A. Theater Additions. A stage addition to a live performance theater shall not be considered as part of the height of the structure under the following circumstances:
12 1. The stage addition is devoted solely to rigging fly systems,

2. The addition is made to a theater that existed as of December 31, 2003, and

3. The stage addition does not exceed the height of the theater as such theater existed on December 31, 2003.

SECTION 3. Correction of these clerical errors is categorically exempt from the California Environmental Quality Act under Section 15305 of the CEQA Guidelines [Minor Alterations in Land Use Limitations] and Santa Barbara Municipal Code (SBMC) section

22.100.070.C.5 Minor Alterations in Land Use Limitations. SBMC Section

22.100.070.C.5(c) specifies that categorical exemptions in this class include “Adoption or amendment of land use or development ordinances, regulations, standards, or guidelines that substantially maintain existing land use intensity or density.” The listed clerical corrections have no impact on existing land use intensity or density.

ORDINANCE NO._____

AN ORDINANCE OF THE COUNCIL OF THE CITY OF

SANTA BARBARA AUTHORIZING THE WATERFRONT

DIRECTOR TO EXECUTE THE CONSENT TO ASSIGNMENT
OF LEASE AGREEMENT NO. 28,360, FORMERLY KNOWN
AS AGREEMENT NO. 27,073, WITH CAPTAIN’S COVE LLC
TRANSFERRING 51% OF MS. AMY ROSE’S CAPITAL
INTEREST TO MR. BRYAN DEMINIE FOR THE ANCHOR
ROSE LOUNGE, LOCATED DOWNSTAIRS AT 113

HARBOR WAY, COMMENCING UPON THE EFFECTIVE
DATE OF THE ENABLING ORDINANCE.

THE COUNCIL OF THE CITY OF SANTA BARBARA DOES ORDAIN AS FOLLOWS:

SECTION 1. In accordance with the provisions of Section 521 of the Charter of the City of Santa Barbara, the consent to assignment of lease agreement with Captain’s Cove LLC transferring 51% of Ms. Amy Rose’s capital interest to Mr. Bryan Deminie for the Anchor Rose Lounge, located downstairs at 113 Harbor Way, commencing upon the effective date of the enabling Ordinance, a copy of which is on file with the City Clerk, is approved.

Agenda Item No. 4
File Code No. 570.03
ORDINANCE NO._____

AN ORDINANCE OF THE COUNCIL OF THE CITY OF

SANTA BARBARA AUTHORIZING THE WATERFRONT

DIRECTOR TO EXECUTE THE CONSENT TO ASSIGNMENT
OF LEASE AGREEMENT NO. 28,361, FORMERLY KNOWN
AS AGREEMENT NO. 23,328, WITH CAPTAIN’S COVE LLC
TRANSFERRING 51% OF MS. AMY ROSE’S CAPITAL
INTEREST TO MR. BRYAN DEMINIE FOR THE ANCHOR
ROSE RESTAURANT, LOCATED UPSTAIRS AT 113

HARBOR WAY, COMMENCING UPON THE EFFECTIVE
DATE OF THE ENABLING ORDINANCE.

THE COUNCIL OF THE CITY OF SANTA BARBARA DOES ORDAIN AS FOLLOWS:

SECTION 1. In accordance with the provisions of Section 521 of the Charter of the City of Santa Barbara, the consent to assignment of lease agreement with Captain’s Cove LLC transferring 51% of Ms. Amy Rose’s capital interest to Mr. Bryan Deminie for the Anchor Rose Restaurant, located upstairs at 113 Harbor Way, commencing upon the effective date of the enabling Ordinance, a copy of which is on file with the City Clerk, is approved.

Agenda Item No. 5
File Code No. 570.03
ORDINANCE NO._____

AN ORDINANCE OF THE COUNCIL OF THE CITY OF

SANTA BARBARA APPROVING AND AUTHORIZING THE

WATERFRONT DIRECTOR TO EXECUTE THE
AMENDMENT TO LEASE AGREEMENT NO. 15,309,
FORMERLY KNOWN AS AGREEMENT NO. 10,356 WITH

STEARNS WHARF COMPANY, DBA MOBY DICK

RESTAURANT, DEFERRING THREE MONTHS BASE
RENT WITH LEASE TERMS ENDING DECEMBER 31, 2030,

COMMENCING UPON THE EFFECTIVE DATE OF THE
ENABLING ORDINANCE.

THE COUNCIL OF THE CITY OF SANTA BARBARA DOES ORDAIN AS FOLLOWS:

SECTION 1. In accordance with the provisions of Section 521 of the Charter of the City of Santa Barbara, an Ordinance of the Council of the City of Santa Barbara Approving the Amendment to Lease Agreement No. 10,356 With Stearns Wharf Company, DBA Moby Dick Restaurant, upon the effective date of the enabling ordinance.

Agenda Item No. 6
File Code No. 570.03

CITY OF SANTA BARBARA

COUNCIL AGENDA REPORT
AGENDA DATE: March 12, 2024
TO: Mayor and Councilmembers
FROM: Airport Department
SUBJECT: Resolution to Authorize Airport Patrol to Join California Peace Officer Standards and Training [Resolution]
RECOMMENDATION:

DISCUSSION:

Background
The Santa Barbara Airport Patrol serves as the Law Enforcement Division of the Airport Department for the City of Santa Barbara. Airport Patrol Officers are sworn Peace Officers under California Penal Code § 830.33(d) and are required under Federal law to provide armed law enforcement services at the Santa Barbara Municipal Airport. The Airport Patrol originally began as a security organization and has evolved into a highly trained and focused specialized law enforcement organization in response to emerging threats to civil aviation.

The Commission on Peace Officer Standards and Training (POST) is the State regulatory body that sets the minimum standards for selection and training of peace officers in California. The mission of POST is to continually enhance the professionalism of California law enforcement in serving its communities. POST participating agencies agree Agenda Item No. 7
File Code No. 560.01
Council Agenda Report
Resolution to Authorize Airport Patrol to join California Peace Officer Standards and Training [Resolution]
March 12, 2024

Page 2 ends

to comply with and continue to adhere to minimum selection and training standards and all Commission Regulations. Currently, more than 600 agencies in the State of California participate in the POST Program and thereby have access to POST’s vast services.

Federal Requirements for Airport Law Enforcement:

Pursuant to Title 49 CFR 1542.215, the Santa Barbara Airport is required to provide uniformed law enforcement personnel in the number and manner necessary to support its Airport Security Program and Federal Transportation Security screening operations.

Those Law Enforcement Officers must be available and committed to respond to any incident in support of a civil aviation security program or commercial air carrier program.

Airport Law Enforcement Officers employed by the Santa Barbara Airport must be armed, have the authority to arrest with or without a warrant, be identifiable by appropriate indicia of authority and meet the training standard for law enforcement officers prescribed by either the State or local jurisdiction in which the airport is located for law enforcement officers performing comparable functions.

POST Membership Process:

POST Commission Regulations and California law establish the procedures necessary for the Airport Patrol to become a participating member of the POST Program. Under POST Commission Regulation 1010, prospective agencies must comply with the following procedures:
 Adopt a Resolution wherein the city and its law enforcement agency agree to abide by the training and recruitment standards for peace officers as enumerated in section 13510 of the California Penal Code (PC). The Resolution must also authorize the Commission to make inquiries as deemed necessary to ascertain that the city’s peace officer personnel adhere to the aforementioned standards, pursuant to 13512 PC. Finally, the Resolution must also include a request from the city to receive aid from the State of California as codified in 13522 PC.  Submit to the Commission a letter of intent to participate in the POST program.

The letter shall be accompanied by a copy of the signed Resolution mentioned above.
 Following the submission of the letter of intent and signed Resolution, POST staff will work with the prospective agency to ensure each incumbent officer is compliant with POST minimum training standards that were applicable at the time of each officer's appointment.
 POST participating agencies shall require every peace officer, appointed on or after the agency's entry into the POST Program, to attend a full POST Basic Council Agenda Report
Resolution to Authorize Airport Patrol to join California Peace Officer Standards and Training [Resolution]
March 12, 2024

Page 3 ends

Academy and earn a POST Basic Certificate as specified in Commission Regulation 1202(a)(5).
 When the agency is found to be in full compliance, participation will be confirmed by the Executive Director of POST and an effective date of entry established.

Approval of an agency’s participation in the POST Reimbursable Program will also be confirmed.

Budget / Financial Information:

While there is no direct cost to join the POST program, there may be indirect training costs incurred if officers need to attend additional classes to meet POST minimum standards. Specifically, if POST staff determine an incumbent Airport Patrol officer does not meet the minimum POST training standards to remain in that role, then that officer must be sent to additional training to correct the deficiency. Based upon our collective research, the possibility exists that some Airport Patrol officers may lack current proof of law enforcement certification. If so, POST could require those officers to attend a 4-week Requalification Course. The current estimate for tuition, travel, lodging and per diem for the 4-week Requalification Course is about $9,000 per officer.

Unfortunately Post staff will not disclose in advance any officer-specific information related to training or potential deficiencies, so we cannot accurately predict who or how many Airport Patrol officers may need to attend a Requalification Course.

PREPARED BY: Andrew Hill, Sergeant, Santa Barbara Police Department SUBMITTED BY: Christopher Hastert, Airport Director
APPROVED BY: City Administrator's Office
RESOLUTION NO. _____

A RESOLUTION OF THE COUNCIL OF THE CITY OF

SANTA BARBARA ACCEPTING THE REQUIREMENTS OF
SECTIONS 13510, 13512, AND 13522 OF THE CALIFORNIA

PENAL CODE RELATING TO THE RECRUITMENT AND

TRAINING OF PEACE OFFICERS FOR THE AIRPORT
PATROL.

WHEREAS, the City of Santa Barbara declares that it desires to qualify to receive aid from the State of California under the provisions of Section 13522, Chapter 1 of Title 4, Part 4 of the California Penal Code;
WHEREAS, pursuant to Sections 13510 and 13512 of said Chapter 1, the Santa Barbara Airport Patrol will adhere to the standards for recruitment and training established by the Commission on Peace Officer Standards and Training;
WHEREAS, the Commission and its representatives may make such inquiries as deemed necessary to ascertain that the peace officer personnel of the Santa Barbara Airport Patrol adhere to the standards for recruitment and training established by the California Commission on Peace Officer Standards and Training.

NOW, THEREFORE, BE IT RESOLVED BY THE COUNCIL OF THE CITY OF SANTA BARBARA THAT the City of Santa Barbara desires to qualify to receive aid from the State of California under the provisions of Section 13522, Chapter 1 of Title 4, Part 4 of the California Penal Code.

BE IT FURTHER RESOLVED that pursuant to Sections 13510 and 13512 of said Chapter 1, the Santa Barbara Airport Patrol will adhere to the standards for recruitment and training established by the Commission on Peace Officer Standards and Training.

BE IT FURTHER RESOLVED that the Commission and its representatives may make such inquiries as deemed necessary to ascertain that the peace officer personnel of the Santa Barbara Airport Patrol adhere to the standards for recruitment and training established by the California Commission on Peace Officer Standards and Training.

CITY OF SANTA BARBARA

COUNCIL AGENDA REPORT
AGENDA DATE: March 12, 2024
TO: Mayor and Councilmembers
FROM: Downtown Team Division, Public Works Department
SUBJECT: Citywide Traffic Model Update: Adoption of the Travel Model Development Report, Traffic Impact Analysis, Traffic Analysis Guidelines, and Master Environmental Assessment Guidelines for Transportation Analysis [Resolution]
RECOMMENDATION:

That Council adopt, by reading of title only, a Resolution of the Council of the City of Santa Barbara Adopting the Travel Model Development Report, Traffic Impact Analysis, Traffic Analysis Guidelines, and Master Environmental Assessment Guidelines for Transportation Analysis.

EXECUTIVE SUMMARY:

The City of Santa Barbara’s (City) Traffic Model is a tool for planning and operating road traffic infrastructure. It is used to evaluate the impacts of proposed land development projects on traffic congestion and determine the amount of driving that new developments would create. It is a critical component of the process of reviewing land development for consistency with City policy and environmental impacts. Modeling traffic helps explain complex interactions and predict traffic behavior for peak hours and daily miles traveled.

On September 14, 2021, Council approved a contract with Iteris, Inc., to update the Traffic Model to a Travel Model, which now enables the City to evaluate both existing and forecasted traffic congestion (level of service of intersections) and the total vehicle miles traveled. This helps evaluate project-specific and regional impacts of greenhouse gas emissions and the effects of climate change.

On December 21, 2023, the Planning Commission unanimously recommended that Council adopt the updated Travel Model Development Report, 2035 General Plan Update Traffic Impact Analysis, Traffic Analysis Guidelines, and Master Environmental Assessment Guidelines for Transportation Analysis.

Agenda Item No. 8
File Code No. 670.09
Council Agenda Report
Citywide Traffic Model Update: Adoption of the Travel Model Development Report, Traffic Impact Analysis, Traffic Analysis Guidelines, and Master Environmental Assessment Guidelines for Transportation Analysis [Resolution]
March 12, 2024

Page 2 ends

DISCUSSION:

Background
The City’s Traffic Model was created in 2008 and adopted with the General Plan Update in 2011. Ideally, the Traffic Model is updated every five to seven years to maintain accuracy.

It was last updated in 2015. In 2021, the City contracted with a consultant, Iteris, Inc., and as mentioned updated the Traffic Model to a Travel Model.

The consultant’s scope of work included a Model Development Report explaining how traffic and travel patterns were modeled; a Traffic Impact Analysis for the General Plan, Housing Element, and Climate Action Plan Updates with a forecast year of 2035. The process of updating and validating the Travel Model also resulted in minor changes to the Land Development Team Traffic Analysis guidelines to reflect impacted intersections, as well as the creation of the new Master Environmental Assessment Guidelines for Transportation Analysis that evaluates vehicle miles traveled, which became the State’s new environmental metric under the California Environmental Quality Act (CEQA) in 2020. In 2020, City Council adopted the updated Traffic Management Strategy to retain traffic congestion analysis as a City policy.

Updated Travel Model
Iteris, Inc. updated the City’s Traffic Model to a Travel Model based on the Santa Barbara County Association of Governments’ (SBCAG) regional Travel Model. The City Travel Model (SBTM) is a focused citywide model of measuring traffic congestion (levels of service at intersections - LOS) at peak periods and vehicle miles traveled (VMT).

The Travel Model uses known traffic data from a baseline year along with information about projected changes to land use and circulation patterns to forecast LOS and VMT for future years. The Travel Model for the City follows a standard four-step modeling process, which includes Trip Generation, Trip Distribution, Mode Split, and Traffic Assignment. Refer to Attachment 1- Model Development Report for the City of Santa Barbara Travel Demand Forecasting Development, Documentation, and User’s Guide for details.

To calculate Trip Generation, Transportation Analysis Zones (TAZs), or geographic areas of similar land use or land activity, were created within the County. After zone refinement, the SBTM contains a total of 1,202 TAZs (County-wide), including 367 City TAZs. The City’s Planning Division provided data on existing and proposed land use from 2020 to forecasted land use in 2035. Below is a summary of the General Plan Update Net Land Use Changes provided by the City:

Council Agenda Report
Citywide Traffic Model Update: Adoption of the Travel Model Development Report, Traffic Impact Analysis, Traffic Analysis Guidelines, and Master Environmental Assessment Guidelines for Transportation Analysis [Resolution]
March 12, 2024

Page 3 ends

Land Use Type Existing (2020) Proposed Land
Use Plan (2035) Net Change
Residential
Residential Units 37,621 units 43,236 units +5,615 units Population 88,485 residents 100,713 residents +12,228 residents Nonresidential
Employment 77,153 jobs 82,867 jobs +5,714 jobs
The land use data were assigned to TAZs and converted into socio-economic data including zonal, population, household, student, income, employment, and parking information. The socio-economic data was then used as a direct input into the travel model to determine how these changes in land use would be expected to affect trip generation.

After establishing parameters for Trip Generation, Trip Distribution is modeled by estimating trips from one location (origin) to another (destination). For example, a trip may be from home to school or work, from home to neighborhood services, or other types of destinations. Next, the Travel Model identifies how each trip was made (trips by vehicle, walk/bike, or transit), which is known as Mode Split. Finally, Traffic Assignment chooses the specific travel route between locations for any given trip in a particular time period.

This gives an estimate of the volumes of each mode of travel that are on the road network at any time of day.

The Travel Model was validated for 2020 baseline year conditions (pre-pandemic) and was then used to create year 2035 traffic forecasts for the General Plan, Housing Element, and Climate Action Plan Updates as well as for Land Development projects for projectspecific traffic impact analysis and/or project-specific VMT analysis. Validating the Travel Model requires comparing estimated and observed traffic on the roadway. From these comparisons, current impacted intersections and forecasted impacted intersections can be determined.

Traffic Impact Analysis and Updated Traffic Analysis Guidelines The Travel Model was used to conduct a Traffic Impact Analysis. Although traffic congestion (i.e. LOS) is no longer used for evaluating the environmental impact of a project under CEQA, in June 2020, Council amended the Traffic Management Strategy to ensure that traffic congestion remained an important land use and development review criteria for nonresidential projects subject to Santa Barbara Municipal Code Chapters

28.85 (Coastal Zoning Ordinance) and 30.170 (Inland Zoning Ordinance). The General Plan states that Santa Barbara will “create a more integrated multi-modal transportation system to connect people, places, goods, and services by providing a choice of Council Agenda Report
Citywide Traffic Model Update: Adoption of the Travel Model Development Report, Traffic Impact Analysis, Traffic Analysis Guidelines, and Master Environmental Assessment Guidelines for Transportation Analysis [Resolution]
March 12, 2024

Page 4 ends

transportation modes and decreasing vehicle traffic congestion.” Thus, project-related traffic effects continue to be analyzed using the same LOS process, methodology, and congestion standards, as a City policy instead of as a CEQA threshold.

The Traffic Impact Assessment included 50 study intersections throughout the City (refer to Attachment 2, Figure 1: Study Intersection Locations). Traffic operations at the study intersections were evaluated during the weekday morning (7:00 to 9:00 AM) and evening (4:00 to 6:00 PM) peak hours with a baseline year of 2020 and a future year of 2035. The Traffic Impact Analysis identified 27 of the 50 studied intersections that exist or are forecasted to be impacted, which means the intersections are operating below the General Plan-adopted threshold during peak hours of LOS “C” or 0.77 or greater ratio of traffic volume to intersection capacity (V/C) at signalized intersections or average delay of between 15 and 25 seconds at unsignalized, stop sign-controlled intersections or roundabouts. Refer to Attachment 2, Figure 3: Impacted Study Intersections.

The list of impacted intersections was updated in the Land Development Team Traffic Analysis Guidelines (Attachment 3) along with minor edits to the narrative that are reflected as “red”/underlined text. The Land Development Team’s Transportation Planning Staff uses the Traffic Analysis Guidelines when reviewing proposed development projects to ensure a proposed project’s estimated net peak-hour traffic generation would not use a disproportionate share of Santa Barbara’s remaining roadway and intersection capacity. A disproportionate share is defined as one percent of capacity at any of the identified impacted intersections currently not operating within City Standards or forecasted to reach the City by 2035.

Master Environmental Assessment Guidelines for Transportation Analysis In addition to the Travel Model, Iteris, Inc. prepared Master Environmental Assessment Guidelines for Transportation Analysis (Attachment 4). On July 1, 2020, Senate Bill 743 (SB 743) went into effect, revising the criteria for analyzing transportation impacts of projects under CEQA. Under the new criteria, travel delays or traffic congestion effects measured by intersection LOS standards are not considered a significant impact on the environment. Instead, VMT was identified as the most appropriate measure of the transportation impacts of a project. VMT refers to the amount and distance of automobile travel attributable to a project. The purpose of SB 743 was to ensure environmental impacts of traffic, such as noise, air pollution, greenhouse gas emissions, and safety concerns, are properly addressed and mitigated through CEQA, while encouraging land use and transportation planning decisions that reduce VMT, such as infill development, public transit, and non-motorized transportation.

Since SB 743 went into effect, the City has been using the State’s CEQA Guidelines as well as the Governor’s Office of Planning and Research Technical Advisory on Evaluating Transportation Impacts in CEQA dated 2018. The State provides screening criteria to quickly identify projects not expected to result in transportation impacts under the VMT methodology. Consistent with CEQA Guidelines §15064.3, projects in areas that are Council Agenda Report
Citywide Traffic Model Update: Adoption of the Travel Model Development Report, Traffic Impact Analysis, Traffic Analysis Guidelines, and Master Environmental Assessment Guidelines for Transportation Analysis [Resolution]
March 12, 2024

Page 5 ends

already well served by a major transit stop or high-quality transit corridor are presumed to have less than significant transportation impacts. A major transit stop is defined in the Guidelines as a site containing an existing rail transit station, a ferry terminal served by either a bus or rail transit service, or the intersection of two or more major bus routes with frequencies of service intervals of 15 minutes or less during the morning and afternoon peak commute periods. A high-quality transit corridor is defined as an existing corridor with a fixed route bus service with service intervals no longer than 15 minutes during peak commute hours. An existing stop along a high-quality transit corridor may include a planned and funded stop that is included in an adopted regional transportation improvement program. Since much of the City is within one-half mile of either an existing major transit stop or a stop along an existing high-quality transit corridor (this includes most of Downtown, Eastside, Westside, Waterfront, and Upper State Street), most projects would not have significant transportation impacts.

For the few City projects that might result in a significant transportation impact, Iteris, Inc. developed the Master Environmental Assessment Guidelines for Transportation Analysis (Attachment 4) as a supplement to the State’s screening criteria. The City’s CEQA Transportation Assessment Guidelines include analysis methodology, significance thresholds, and mitigation measures to address potential significant impacts in accordance with Title 14, Natural Resources, Division 6, California Natural Resources Agency, Chapter 3: Guidelines for the Implementation of the California Environmental Quality Act. The City’s Guidelines are also consistent with the Governor’s Office of Planning and Research Technical Advisory on Evaluating Transportation Impacts in CEQA dated 2018.

A project’s VMT will be analyzed and assessed on a per capita, per employee, or per service population (population and employees) basis to use an efficiency-based metric.

These are compared to a threshold value to determine potential significant transportation impact. Transportation Planning Staff will use the Guidelines’ Figure 2: Land Development Project CEQA Screening Process (Attachment 4) to determine if a modellevel VMT analysis is required using the City’s Travel Model. If it is determined the project would result in an impact, the City’s CEQA Transportation Assessment Guidelines contain VMT strategies to reduce a project to a less than significant level. Some examples of VMT reduction include on and off-site strategies for parking management, trip reduction, land use, and neighborhood design.

ENVIRONMENTAL REVIEW:

The adoption of new local CEQA thresholds of significance for transportation impacts and the adoption of the City’s updated Traffic Model will not have a significant environmental impact and are exempt from CEQA, pursuant to Section 15308, because the actions are undertaken by the City for the protection of the environment. The revised CEQA thresholds will be compliant with State mandate SB 743 and will be used in a regulatory process (CEQA process) that involves procedures for the protection of the environment.

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BUDGET/FINANCIAL INFORMATION:

This action has no financial/budget impact on the City.

ATTACHMENTS: 1. Model Development Report for the City of Santa Barbara Travel Demand Forecasting Development, Documentation, and User’s Guide

2. Iteris Traffic Impact Analysis

3. Land Development Team Traffic Analysis Guidelines

4. Master Environmental Assessment Guidelines for Transportation Analysis
PREPARED BY: Sarah Clark, Downtown Team Manager/JWG/nv
SUBMITTED BY: Clifford M. Maurer, P.E., Public Works Director APPROVED BY: City Administrator’s Office
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting
Development, Documentation,
and User’s Guide
(SBTM)
October 25, 2023
Submitted to:
11487.22 | Prepared by Iteris, Inc.

ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | i

TABLE OF CONTENTS
1 Introduction ..................................................................................................................................................1

1.1 Study Area............................................................................................................................................1

1.2 Model Description................................................................................................................................1 2 Model Components......................................................................................................................................2

2.1 SBTM Structure ....................................................................................................................................2

2.2 SBTM Components...............................................................................................................................3

2.3 Transportation Analysis Zones.............................................................................................................3

2.4 Land Use...............................................................................................................................................5

2.5 Networks..............................................................................................................................................8

2.6 Trip Generation..................................................................................................................................13

2.7 Trip Distribution ................................................................................................................................. 18

2.8 Mode Choice ......................................................................................................................................18

2.9 Time of Day Factoring ........................................................................................................................18

2.10 Traffic Assignment..............................................................................................................................18

2.11 Air Quality Data Module ....................................................................................................................19 3 MODEL CALIBRATION AND VALIDATION....................................................................................................23

3.1 Model Calibration...............................................................................................................................23

3.2 Assignment Validation .......................................................................................................................26

3.3 Conclusion..........................................................................................................................................30 4 FORECAST YEAR MODEL .............................................................................................................................31

4.1 Transportation Analysis Zones...........................................................................................................31

4.2 Networks............................................................................................................................................31

4.3 Mode Choice ......................................................................................................................................32

4.4 Air Quality Data Module ....................................................................................................................34 5 User’s Guide................................................................................................................................................39

5.1 Installing the SBTM ............................................................................................................................40

5.2 Defining Scenarios in the SBTM .........................................................................................................40

5.3 Launching the SBTM...........................................................................................................................40

5.4 Select Link Analysis.............................................................................................................................42

APPENDICES
Appendix A – SBCAG Travel Model Documentation
Appendix B – Traffic Analysis Zones
Appendix C – Land Use and Socioeconomic Data (SED)
Appendix D – SB 375 Vehicle O-D Summaries
Appendix E – Air Quality Summaries
Appendix F – SB 743 VMT Summaries
Appendix G – Model Validation
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | ii
TABLES
Table 2-1: SBTM Zone Structure ...........................................................................................................................4 Table 2-2: City of Santa Barbara Land Use............................................................................................................5 Table 2-3: SBTM Non-Residential Land Use to Employment Factors...................................................................6 Table 2-4: SBTM Socioeconomic Categories.........................................................................................................7 Table 2-5: Facility Type Coding .............................................................................................................................8 Table 2-6: Roadway Capacity Assumptions..........................................................................................................9 Table 2-7: Base Year Transit Route Summary.....................................................................................................10 Table 2-8: Trip Purposes.....................................................................................................................................13 Table 2-9: Trip Productions................................................................................................................................. 13 Table 2-10: SBTM HBW (Home-Based Work) Trip Production Rates ................................................................. 14 Table 2-11: SBTM HBSC (Home-Based School) Trip Production Rates...............................................................14 Table 2-12: SBTM HBO (Home-Based Other) Trip Production Rates..................................................................14 Table 2-13: SBTM NHBW (Non-Home-Based Work) Trip Production Rates.......................................................15 Table 2-14: SBTM NHBO (Non-Home-Based Other) Trip Production Rates.......................................................16 Table 2-15: Employment-based Attraction Trip Rates........................................................................................17 Table 2-16: Trip Attractions................................................................................................................................17 Table 2-17: Special Generator Trips....................................................................................................................17 Table 2-18: SBTM Base Year Regional Trip VMT (in 1,000s) Matrix ...................................................................19 Table 2-19: SBTM Base Year City Trip VMT Matrix .............................................................................................20 Table 2-20: SBTM Base Year (2020) Regional Air Quality Summary by Speed Bin .............................................21 Table 2-21: SBTM Base Year Regional Air Quality Summary by Facility Type.....................................................22 Table 2-22: SBTM Base Year SB 743 VMT...........................................................................................................22 Table 3-1: Trip Generation Validation.................................................................................................................23 Table 3-2: Trip Distribution Validation................................................................................................................24 Table 3-3: 2020 SBTM Peak Mode Choice by Trip Purpose Summary Statistics (Santa Barbara County)..........25 Table 3-4: 2020 SBTM Off-Peak Mode Choice by Trip Purpose Summary Statistics (Santa Barbara County)....25 Table 3-5: SBTM Screenline Validation...............................................................................................................28 Table 3-6: Count/Volume Comparison By Facility Type .....................................................................................29 Table 4-1: SBTM Population, Households, and Employment Compared to SBCAG ...........................................31 Table 4-2: SBTM Population, Households, and Employment.............................................................................31 Table 4-3: Forecast Year Transit Route Summary...............................................................................................32 Table 4-4: 2035 SBTM Peak Mode Choice by Trip Purpose Summary Statistics (Santa Barbara County)..........33 Table 4-5: 2035 SBTM Off-Peak Mode Choice by Trip Purpose Summary Statistics (Santa Barbara County)....33 Table 4-6: SBTM Forecast Year Regional Trip VMT (in 1,000s) Matrix ...............................................................34 Table 4-7: SBTM Forecast Year City Trip VMT Matrix.........................................................................................35 Table 4-8: SBTM Forecast Year Regional Air Quality Summary by Speed Bin ....................................................36 Table 4-9: SBTM Forecast Year Regional Air Quality Summary by Facility Type ................................................37 Table 4-10: SBTM Base vs. Forecast Year VMT Growth......................................................................................37 Table 4-11: SBTM Forecast Year SB 743 VMT.....................................................................................................38 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | iii

FIGURES
Figure 2-1: SBTM Flow-Chart...............................................................................................................................3 Figure 2-2: SBTM TAZ Structure............................................................................................................................4 Figure 2-3: SBTM Base Year Network .................................................................................................................11 Figure 2-4: Base Year Transit Network ...............................................................................................................12 Figure 3-1: SBTM Screenline Locations...............................................................................................................27 Figure 3-2: SBTM Screenline Verification (Maximum Desirable Deviation) .......................................................29 Figure 3-3: SBTM Count versus Volume Comparison .........................................................................................30 Figure 5-1: Model Add-in Component................................................................................................................39 Figure 5-2: SBTM Model Run Setup....................................................................................................................41

ACRONYMS
BPR Bureau of Public Roads
CEQA California Environmental Quality Act
FHWA Federal Highway Administration
GHG Greenhouse Gas
HBO Home-Based Other
HBSch Home-Based School
HBShop Home-Based Shopping
HBW Home-Based Work
HOV High-Occupancy Vehicle
I Interstate
MPH Miles Per Hour
NCHRP National Cooperative Highway Research Program
NHBO Non-Home-Based Other
NHBW Non-Home-Based Work
OD Origin-Destination
PA Production-Attraction
PeMS Performance Measurement System
RTP Regional Transportation Plan
SB Senate Bill
SBCAG Santa Barbara County Association of Governments
SED Socioeconomic data
SR State Route
TAZ Transportation Analysis Zone
V/C Volume to Capacity
VDF Volume Delay Function
VHD Vehicle Hours of Delay
VHT Vehicle Hours Traveled
VMT Vehicle Miles Traveled
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 1
1 INTRODUCTION
This report summarizes the results for development of the City of Santa Barbara Travel Model (SBTM), a focused city-wide model consistent with regional and subregional models, including the base-year and forecast year land-use projections and transportation networks. The SBTM was developed as a refined model for the City of Santa Barbara, built from the current SBCAG regional model including the full Santa Barbara County. The SBTM was developed to address the City of Santa Barbara General Plan Update, transportation impact assessments, vehicle miles traveled (VMT) forecasts for CEQA analysis, and air quality greenhouse gas (GHG) applications.

The previous version of the City of Santa Barbara model was validated to the year of 2008. The documentation in this report reflects an update of the base year to 2020.

1.1 Study Area
The study area of the model includes the full extents from the Santa Barbara County travel demand model.

1.2 Model Description
The base year of 2015 and the forecast years of 2035 and 2050 were obtained from Santa Barbara County. The updated travel demand model was developed using the TransCAD software package. The forecasting model for the City of Santa Barbara is consistent with the traditional four step modeling process, which includes trip generation, trip distribution, mode split, and traffic assignment. Each of these model steps is briefly described in this report with respect to some of the unique characteristics of the SBTM.

The following sections provide an overview of the steps that were followed in the development and validation of the updated SBTM. Appendix A is the full draft documentation of the SBCAG Travel Model.

ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 2
2 MODEL COMPONENTS
This section of the report documents model inputs, variables, and parameters used in the SBTM. The review and revision of the model variables discussed in this memo was an ongoing process throughout the development and validation of the 2020 Base Year model calibration. The intention of modifying variables or parameters is to ensure that the SBTM tool is tailored to meet the needs of the City of Santa Barbara in supporting local and regional projects. Model inputs, variables, and parameters used within the SBTM are important to define and validate to ensure that the SBTM closely replicates existing travel within the City of Santa Barbara.

2.1 SBTM Structure
The SBTM replicates the SBCAG Travel Model. Figure 2-1 illustrates the basic SBTM flow chart, including model improvements developed for the SBTM. A more detailed flow chart showing each of the modeled steps within each model stage is summarized in Appendix A. General descriptions of each of the steps are below, as well as in Appendix A:
• Trip Generation: a prediction of the total number of person trips produced by households and attracted to locations of employment.
• Skims: a zone-to-zone calculation of impedances (examples include travel times, distances, and costs), which is based on the roadway network and used in trip distribution. • Trip Distribution: a zone-to-zone representation of the distribution of person trips from the trip generation stage, utilizing impedances from the skimming stage. These person trips are stored in a matrix and are in production-attraction format.
• Mode Choice: a zone-to-zone representation of the choice of transportation mode. These person trips by mode are stored in a matrix and are in production-attraction format. • PA to OD: a zone-to zone calculation converting person trips into modal trips (vehicle and nonmotorized) and converted to origin and destination format. • Motorized Assignment: An assignment of vehicular travel. • Bike & Walk Assignment: An assignment of non-motorized travel.

ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 3
Figure 2-1: SBTM Flow-Chart

2.2 SBTM Components
The development of the SBTM included six main steps, as follows:
• Conversion of land use to socioeconomic data within the City of Santa Barbara • Calibration of model components
• Network refinements and adjustments
• Automated model outputs for VMT, VHT, and VHD
• Automated model outputs for SB 743 VMT
• Automated plotting generator, including functional classification, number of lanes, posted speed assumptions, and V/C ratio (as a representation of level-of-service)

2.3 Transportation Analysis Zones
Transportation analysis zones (TAZs) are geographic areas dividing a planning region into relatively similar areas of land use or land activity. TAZs should be relatively homogenous in character and usually lie along major roadways, follow significant physical features, City, or other political boundaries. The TAZ structure for the SBTM was obtained through coordination with the City of Santa Barbara, and use of the SBCAG and previous City of Santa Barbara model TAZ structures. After zone refinement, the SBTM has a total of 1202 zones.

Figure 2-1 illustrates the zone structure within the SBTM for the City of Santa Barbara and Table 2-1 summarizes the City of Santa Barbara zone structure for the current and SBTM. Detailed zone structure figures are included in Appendix B.

Trip Generation
Network Skimming
Trip Distribution
Mode Choice
Production/Attraction to Origin-Destination
Motorized Assignment
Bike & Walk Assignment
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 4
Figure 2-2: SBTM TAZ Structure
Table 2-1: SBTM Zone Structure

MODEL AREA SBTM
Zone Sequence
City of Santa Barbara
10002-10004, 10006-10009, 10012-10014, 10017-10025
10027-10034, 10036-10040, 10045, 10046, 10049-10053
10075, 10078, 10080, 10082, 10084, 10086-10092, 10096-10113 10115-10290, 10293-10426, 10445-10449, 11006, 11115, 11170 20030,20042-20045
City of Buellton 50265,50270,116702,116703,126601-126904 City of Carpinteria 50022,50023,50146,50347,102001-102402,109801,109802 111001-111003,114301-114402,127501-132002,132501,132502 City of Goleta 20002,20004-20015,20019-20026,20037-20041,20046,20049 20053,20054,20056-20080,20093,20125-20160
City of Guadalupe 50149,112102,112103,112201,112202,114801-114803 City of Lompoc 40001-40339
City of Santa Maria 30001-30999,50304
City of Solvang 100701-101003,101201-101205,103101-103103 Unincorporated
10005,10011,10015,10016,10026,10035,10041-10043,10048
10054-10074,10076,10077,10079,10081,10083,10085,10093,10114 10292,10430-10444,10450-10460,11439-20001,20003,20016-20018 20031-20036,20047,20048,20050,20052,20055,20081-20092
20094-20123,20162,50026-50118,50166,100101-100512,
101101-101106,101401-101810,102501-102905,103701-109705 109901-109912,111401-112101,113901-114206,114501-114711 116501-116701,125901-126405,126803,127101-127404,132003 San Luis Obispo County 300001-300008
Ventura County 200001-200032
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 5

2.4 Land Use
The City of Santa Barbara maintains a database of land use data to be used in their travel demand model for existing and future planned years.

Table 2-2 summarizes City of Santa Barbara land use categories. Appendix C includes a summary of the existing year and forecast year City of Santa Barbara land use.

Table 2-2: City of Santa Barbara Land Use
Land Use Category Units
Residential
Single-Family Dwelling Units
Multi-Family Zero Cars Dwelling Units
Multi-Family One Car Dwelling Units
Multi-Family Two Cars Dwelling Units
Multi-Family Three or More Cars Dwelling Units
Non-Residential
Commercial Services Thousand Square-feet
Entertainment Thousand Square-feet
Auto Related Thousand Square-feet
Restaurant Thousand Square-feet
Retail Thousand Square-feet
Lodging Thousand Square-feet
Office Thousand Square-feet
Institutional Thousand Square-feet
Industrial Thousand Square-feet
Hospital Thousand Square-feet
Religious Facilities Thousand Square-feet
Police and Fire Services Thousand Square-feet
Elementary and Middle School Students
High Schools Students
Colleges Students
Recreation (Parks and Beaches) Relative Popularity*
Golf Acres
SBCAG_Agricultural** Employees
SBCAG_Industrial** Employees
SBCAG_Commercial** Employees
SBCAG_Office** Employees
SBCAG_Service** Employees
* Recreational trips are generated at the home end (either Residential or Lodging) and distributed to the various Recreational areas of the City based on their relative popularity. Relative popularity was calibrated using count data near the recreational sites.
** Data adapted from SBCAG TAZs uses SBCAG units of employment.

Source: Fehr & Peers, 2008.

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2.4.1 Land Use Tool
A key input to the “Land Use to SED” module within the SBTM is land use conversion factors which converts non-residential square footages to number of employees. The land use to SED conversion module is developed in Excel format. It includes the square footage by land use type for each of the TAZs within the City of Santa Barbara. Instructions for using the spreadsheet tool are included in the Model Users Guide.

The conversion factors used in the land use tool were developed using two datasets: existing land use data (from City of Santa Barbara) and existing Socioeconomic Data (from SBCAG). Land Use to total employment conversion factors were developed for non-residential land use categories by evaluating homogenous zones.

Land use to employment factors are summarized in Table 2-3, and are used in the land use to SED conversion tool. As a note, these are generalized land use to employment factors for the City.

Table 2-3: SBTM Non-Residential Land Use to Employment Factors LAND USE CATEGORY VALIDATED LAND USE (SQ. FT.)

TO TOTAL EMPLOYMENT RATE
Agriculture 0.02
Commercial 3.66
Entertainment 5.15
Auto 3.32
Restaurant 7.16
Retail 5.14
Hotel 5.64
Office 2.63
Institutional 7.14
Industrial 3.52
Hospital 5.25
Safety (including police and fire services) 9.17
Church 3.68
Additional residential land use inputs required for each zone include the following attributes:
• Number of Households – Broken down into age, income level, and number of people living in a household.
• Number of Hotel/Motel Rooms – The total number of hotel or motel rooms in each zone.

2.4.2 Socioeconomic Data
The land use tool develops the required SED input data for all zones in the model, and which is used as direct input into the travel demand model for trip generation. Table 2-4 summarizes the model SED categories used in the SBTM. The input SED for the model years is included in Appendix C.

Socioeconomic data is inclusive of zonal, population, household, student, income, employment, and parking information.

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Table 2-4: SBTM Socioeconomic Categories
Land Use Category Description
Households # of households for a TAZ
IncomeCategory Average income category of TAZ (1-8)
1: < $15K
2: $15-$25k
3: $25-$50k
4: $50-$75k
5: $75-$100k
6: $100-$150k
7: $150-$200k
8: > $200k
HH_People <18 Number of HH with people under 18
HH_No People <18 Number of HH with no people under 18
HH_People 65+ Number of HH with people >= 65
HH_No People 65+ Number of HH with no people >= 65
HH_1_person 1 person HH
HH_2_persons 2 person HH
HH_3_persons 3 person HH
HH_4+_persons 4+ person HH
HU_Owner Households with an Owner occupant
HU_Renter Households with renter occupants
HH_IncomeCat1,2,…8 Household income by category
1: < 15K
2: 15-25K
3: 25-50K
4: 50-75K
5: 75-100K
6: 100-150K
7: 150-200K
8: > 200K
Single_Family_HH # of Single-family households
Multi_Family_HH # of Multi-family households
Agricultural_Employment # of Agricultural employees
Commercial_Employment # of Commercial employees
Industrial_Employment # of Industrial employees
Office_Employment # of Office employees
Service_Employment # of Service employees
Education_Employment # of Education employees
Total_Employment # of Total employees
Low_Density_Residential Low density residential dwelling units High_Density_Residential High density residential dwelling units Low_Density_Commercial Low density commercial floor space (in 1000sf units) High_Density_Commercial High density commercial floor space Office Office floor space
Industry Industrial floor space
Institutional Institutional floor space
PreSchool_Students Preschool students
Elementary_Enrollment Elementary school enrollment (public) Middle_School_Enrollment Middle school enrollment
High_School_Enrollment High school enrollment
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Land Use Category Description
College_Enrollment College enrollment
Private_Elementary_Enrollment Private elementary school enrollment (K-8) Private_High_Enrollment Private high school enrollment
Hotels_Motels Hotel/motel rooms
Agricultural Agricultural floor space
Parks_and_Open_Space Parks and open space acres
UCSB Number of UCSB students
Student_Housing_On_Campus Number of UCSB students living on campus Student_Housing_Off_Campus Number of UCSB students living off campus Faculty_Staff Number of UCSB faculty and staff
La_Cumbre_Mall La Cumbre Mall Floor space

2.5 Networks
The baseline base year (2020) and forecast year (2035) networks were obtained from SBCAG through the development of the subregional model. The 2020 base year was verified for existing (2020) estimated conditions, and the forecast 2035 year was not modified.

The networks for the SBTM are maintained in a master roadway network, consistent with the process for the SBCAG model files.

2.5.1 Highway Network
The model network is inventoried within the geo-database with multiple attributes, and is discussed in depth in the SBCAG Model documentation, included in Appendix A. Primary attributes which are critical to the performance of the travel demand model include:
• Speed Limits
• Functional Class
• Bike Class
• Number of Lanes (by time period)
• Directionality (one-way versus two-way streets)
• Mode
Table 2-5 summarizes the facility type coding of the highway network. The roads in the network are classified into 16 categories. The SBTM includes roadways that are classified into higher functional class and local roads.

Table 2-5: Facility Type Coding

NETWORK CODE DESCRIPTION
1 Freeway
2 Expressway
3 Principal Arterial
4 Minor Arterial
5 Urban Collector
6 Rural Collector
8 Local Roads
9 Centroid Connectors
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NETWORK CODE DESCRIPTION
10 Ramp
11 System-to-System Ramp
12 HOV Links
13 Roundabout
15 Exclusive Bike Trail
99 AMTRAK Rail Link
999 Connector link between AMTRAK and Highway Network
Inherent to the SBCAG model are roadway network speeds and capacities, as summarized in Table 2-6.

Capacity assumptions in the SBTM are identical to the assumptions in the SBCAG model.

Figure 2-2 illustrates the SBTM base year highway network.

Table 2-6: Roadway Capacity Assumptions

FACILITY TYPE SBTM
CAPACITY RANGE (VPHPL)*
Freeway 1,900 to 2,400
Expressway 900 to 2,400
Principal Arterial 900 to 1,500
Minor Arterial 750 to 1,200
Urban Collector 700 to 1,100
Rural Collector 700 to 1,100
Local Roads 600 to 1,000
Ramp 1,800
System-to-System Ramp 2,000
HOV 1,850 to 2,500
Roundabout 1,900 to 2,800
*Note: The range of capacities shown for the SBTM are representative of an “Urban” area type.

VPHPL = Vehicles per hour per lane.

2.5.2 Transit Networks
It should be noted that the current version of the SBTM did not maintain a transit network. By developing a subarea model from the SBCAG model, the SBTM inherits the transit capabilities within the entire Santa Barbara County.

Within the City of Santa Barbara, there are multiple transit authorities, and modes of transit. Table 2-7 summarizes the route information within the base year SBTM. Figure 2-3 illustrates the base year model transit network within the City of Santa Barbara. A cursory review of the transit routes appears to adequately represent existing conditions.

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Table 2-7: Base Year Transit Route Summary

TRANSIT AUTHORITY NUMBER OF ROUTES
Amtrak 2
Clean Air Express 5
Greyhound 1
Santa Barbara MTD 27
Santa Barbara Airbus 1
Ventura County Transportation
Commission (VCTC) Coastal Express 1
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Figure 2-3: SBTM Base Year Network
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Figure 2-4: Base Year Transit Network
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2.6 Trip Generation
The process of trip generation estimates daily person trips for average weekday conditions by trip type. Trip productions and trip attractions are estimated for each zone based on socioeconomic variables calculated in the land use to socioeconomic tool. Trip generation procedures in the SBTM are largely consistent with trip generation as documented in the SBCAG model documentation, included in Appendix A. The initial steps in the trip generation process include population synthesis, auto ownership, and the generation of 4D variables. All these initial steps are documented fully in Appendix A.

2.6.1 Trip Production Model
The trip production model estimates trip rates based upon each individual persons’ demographic characteristics and the demographic characteristic of the household that the person lives, both developed in the population synthesis steps. These updated trip purposes were inherited from the SBCAG model. Table 2-8 summarizes the trip purposes in the SBTM.

The trip productions are summarized for the City of Santa Barbara in Table 2-9. Tables 2-10 through 2-14 summarize the trip production rates for all trip purposes in the SBTM.

Table 2-8: Trip Purposes

TRIP PURPOSE TRIP PURPOSE DESCRIPTION
Home-Based Work HBW trips go between a home activity and a work activity at the other end of the trip.

Home-Based Shopping HBSH trips go between a home activity and a shopping activity at the other end of the trip.

Home-Based School HBSC trips include all trips related to students with activity at the home end of the trip and a trip to a K12 activity at the other end of the trip.

Home-Based Other HBO trips go between a home activity and a destination that does not classify as work, shopping, or school. HBO trips include all trips that have one end at a home, but do not fall into the other homebased trip categories. Some examples of HBO trips include visiting another home, obtaining medical services, visiting an office for a non-work purpose, serving a passenger, or making a recreational trip, among other activities.

Non-Home-Based Work NHBW trips go between a non-home activity and a destination that classify as work.

Non-Home-Based Other NHBO trips go between a non-home activity and a destination that does not classify as work. NHBO trips include all trips that have no trip ends at a home or work. Some examples of NHBO trips include non-home-based trips which are destined to shopping, school, serving a passenger, making a recreational trip, or other non-home based trips which do not categorize as NHBW trips.

Table 2-9: Trip Productions

TRIP PURPOSE
City of Santa Barbara Model
(2020)
Home-Based Work (HBW) 92,413
Home-Based Shopping (HBShop) 37,587
Home-Based School (HBSch) 40,355
Home-Based Other (HBO) 165,848
Non-Home-Based Work (NHBW) 58,343
Non-Home-Based Other (NHBO) 72,027
Total 466,574
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Table 2-10: SBTM HBW (Home-Based Work) Trip Production Rates Num of Workers in
HH
Household Income Autos Per
Worker
Person
Trip Rate
1 [0, 25000] All 1.230
1 [25000, 50000] All 1.422
1 [50000, 75000] All 1.452
1 [75000, 100k] All 1.470
1 [100k, 150k] All 1.003
1 [150k, inf] All 1.335
2 [0, 25000] < 1 1.335
2 [0, 25000] >= 1 1.352
2 [25000, 50000] < 1 0.392
2 [25000, 50000] >= 1 1.733
2 [50000, 75000] All 1.532
2 [75000, 100k] All 1.262
2 [100k, 150k] All 1.066
2 [150k, inf] All 1.287
3+ [0, 50000] All 1.085
3+ [50000, 75000] < 1 1.700
3+ [50000, 75000] >= 1 1.055
3+ [75000, 100k] All 1.196
3+ [100k, 150k] All 1.591
3+ [150k, inf] All 1.207
Note: HBW Rates only apply to full time and part time workers Table 2-11: SBTM HBSC (Home-Based School) Trip Production Rates Person Age Person Trip Rate
< 3 0
[3-14] 1.125
[14-17] 0.902
[18-24] 0.729
24+ 0.031
Table 2-12: SBTM HBO (Home-Based Other) Trip Production Rates Number of
Workers in
Household
Household
Size
Household Income Household
Autos
Person Trip
Rate
0 1 [0, 50000] 0 1.011
0 1 [0, 50000] 1 1.208
0 1 [0, 50000] 2+ 2.163
0 1 >= 50K All 1.464
0 2 [0, 50000] 0 or 1 1.189
0 2 [0, 50000] 2+ 1.375
0 2 [50000, 100k] 0 or 1 1.037
0 2 [50000, 100k] 2+ 1.870
0 2 [100k, 150k] All 2.151
0 2 [150k, inf] All 1.771
0 3+ [0, 50000] 0 or 1 0.849
0 3+ [0, 50000] 2+ 1.801
0 3+ [50000, 100k] 0 or 1 1.280
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Number of
Workers in
Household
Household
Size
Household Income Household
Autos
Person Trip
Rate
0 3+ [50000, 100k] 2+ 1.965
0 3+ [100k, 150k] All 2.268
0 3+ [150k, inf] All 2.033
1 1 [0, 50000] 0 or 1 1.731
1 1 [0, 50000] 2+ 0.899
1 1 [50000, 100k] 0 or 1 1.245
1 1 [50000, 100k] 2+ 1.033
1 1 >= 100k All 2.699
1 2 [0, 50000] 0 or 1 0.996
1 2 [0, 50000] 2+ 1.125
1 2 [50000, 100k] 0 or 1 1.413
1 2 [50000, 100k] 2+ 0.927
1 2 [100k, 150k] All 1.353
1 2 [150k, inf] All 0.763
1 3+ [0, 50000] 0 or 1 1.333
1 3+ [0, 50000] 2+ 1.282
1 3+ [50000, 100k] 0 or 1 0.328
1 3+ [50000, 100k] 2+ 2.020
1 3+ [100k, 150k] All 2.070
1 3+ [150k, inf] All 1.587
Table 2-13: SBTM NHBW (Non-Home-Based Work) Trip Production Rates Household
Size
Household Income Number of Kids Under 18 Person Trip
Rate
1 [0, 50,000] All 0.967
1 [50,000, 100k] All 1.260
1 [100k, 150k] All 1.972
1 [150k, inf] All 1.043
2 [0, 50,000] All 0.658
2 [50,000, 100k] 0 1.157
2 [50,000, 100k] 1 or 2+ 0.875
2 [100k, 150k] All 0.526
2 [150k, inf] All 0.757
3+ [0, 50,000] 0 0.148
3+ [0, 50,000] 1 0.379
3+ [0, 50,000] 2+ 0.815
3+ [50,000, 100k] 0 0.555
3+ [50,000, 100k] 1 0.698
3+ [50,000, 100k] 2+ 1.089
3+ [100k, 150k] 0 0.473
3+ [100k, 150k] 1 1.221
3+ [100k, 150k] 2+ 2.036
3+ [150k, inf] 0 1.329
3+ [150k, inf] 1 0.816
3+ [150k, inf] 2+ 1.549
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Table 2-14: SBTM NHBO (Non-Home-Based Other) Trip Production Rates Age Household Size Worker or Not Person Trip
Rate
[0, 17] 1 All 0.000
[0, 17] 2 All 0.547
[0, 17] 3+ 0 0.698
[0, 17] 3+ 1 1.062
[18, 30] 1 All 0.438
[18, 30] 2 0 0.411
[18, 30] 2 1 0.475
[18, 30] 3+ 0 0.424
[18, 30] 3+ 1 0.191
[31, 40] 1 All 0.777
[31, 40] 2 0 1.498
[31, 40] 2 1 0.520
[31, 40] 3+ 0 2.782
[31, 40] 3+ 1 0.973
[41, 50] 1 0 2.523
[41, 50] 1 1 0.570
[41, 50] 2 0 0.992
[41, 50] 2 1 0.592
[41, 50] 3+ 0 1.854
[41, 50] 3+ 1 0.641
[51, 64] 1 0 0.724
[51, 64] 1 1 0.907
[51, 64] 2 0 1.192
[51, 64] 2 1 0.759
[51, 64] 3+ 0 1.845
[51, 64] 3+ 1 0.559
65+ 1 0 0.731
65+ 1 1 0.694
65+ 2 0 0.667
65+ 2 1 0.505
65+ 3+ 0 0.522
65+ 3+ 1 0.127

2.6.2 Trip Attraction Model
Trip attractions model estimates trip rates based on the characteristics of the land use activities attracting the trips such as employment. The trip attractions are summarized for the City of Santa Barbara in Table 2-15.

The trip attractions are summarized for the City of Santa Barbara in Table 2-16.

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Table 2-15: Employment-based Attraction Trip Rates
Table 2-16: Trip Attractions
TRIP PURPOSE City of Santa Barbara Model
(2020)
Home-Based Work (HBW) 125,541
Home-Based Shopping (HBShop) 32,095
Home-Based School (HBSch) 20,609
Home-Based Other (HBO) 219,888
Non-Home-Based Work (NHBW) 53,433
Non-Home-Based Other (NHBO) 87,776
Total 539,342

2.6.3 Special Generators
Special generators are used for unique land uses that cannot be adequately represented by one of the standard land use categories. The trip ends for special generators are determined outside the model process for each specific use and added to the results of trip generation process. Special Generator trips for the City of Santa Barbara area summarized in Table 2-17.

Table 2-17: Special Generator Trips
Special Generator # of Trips
Arroyo Burro County Beach Park 5,010
Earl Warren Showgrounds 1,492
East Beach 366
La Cumbre Mall 2,371
Old Mission Santa Barbara 2,982
Santa Barbara Botanic Gardens 78
Santa Barbara Museum of Arts 728
Santa Barbara Zoo 2,426
Stearn Wharf 2,035
West Beach 953
Trip Purpose
Agriculture
Commercial
Industrial
Service
Office
Household
Pre School
Grade
School
High School
Post
Secondary
School
Unit Employee Employee Employee Employee Employee Dwelling Unit Student Student Student Student
HBW 1.131 0.360 2.751 1.300 0.360 - - - - -
HBShop - 1.674 - 0.118 - - - - - -
HBSchool - - - - - - - 1.452 1.165 0.448
HBO - 1.785 - 1.488 - 1.611 6.167 1.425 - -
NHBW 0.922 - 1.615 0.859 1.660 - - - - -
NHBO - 2.142 - 0.377 - 0.453 3.122 0.489 - -
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2.7 Trip Distribution
Trip distribution procedures in the SBTM are consistent with trip distribution as documented in the SBCAG Model documentation, summarized in Appendix A. The SBCAG model utilizes a gravity model for home-based school (HBSC), non-home-based other (NHBO), non-home-based work (NHBW), IXXI, and Visitor trip purposes.

Destination choice models were applied to all other trip purposes.

2.8 Mode Choice
Mode choice procedures in the SBTM are consistent with mode choice as documented in the SBCAG Model documentation, summarized in Appendix A. The “mode choice” component of a travel demand model identifies what mode of travel is used to make the total trips generated for each zone. During mode choice, trips are proportioned into the following modes of travel:
• Auto
• Drive Alone
• Shared Ride (carpool)
• Non-motorized
• Walk
• Bike
• Transit

2.9 Time of Day Factoring
Time of day procedures in the SBTM are largely consistent with time of day as documented in the SBCAG Model documentation, summarized in Appendix A. No parameters for time-of-day factoring were modified for the development of the SBTM. The model uses eight time periods for highway assignments. The eight highway assignment time periods are as follows:
• AM Peak Period = 7:00 AM to 9:00 AM
• Late AM Time Period = 9:00 AM to 12:00 PM
• Lunch Time Period = 12:00 PM to 2:00 PM
• Early PM Time Period = 2:00 PM to 4:00 PM
• PM Peak Period = 4:00 PM to 6:00 PM
• Evening Time Period = 6:00 PM to 8:00 PM
• Late Evening Time Period = 6:00 PM to 12:00 AM
• Nighttime Period = 12:00 AM to 7:00 AM

2.10 Traffic Assignment
Traffic assignment procedures in the SBTM are largely consistent with traffic assignment as documented in the SBCAG Model documentation, summarized in Appendix A. Traffic assignment is the final step in the four-step travel demand modeling process is the assignment of the vehicle trip tables to the highway network. The traffic assignment process chooses the specific travel route between zone pairs for any given trip for each time period.

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2.11 Air Quality Data Module
Recent CEQA guidelines (SB 743) are directing a shift from a level-of-service (LOS) analysis to a greenhouse gas (GHG) or vehicle-miles-traveled (VMT) based analysis. In addition, transportation models are increasingly being used to develop VMT attributable to individual Cities or Districts to meet the Climate Action Plan (CAP) requirements of SB 375. The air quality data module within the SBTM permits the generation of zonal-based VMT as well as link based VMT to support air quality analysis.

2.11.1 Zone-Based VMT
A zone-based VMT calculation identifies the total number of trips produced by, or attracted to, a zone and uses the model distance “skims” to calculate a VMT. The “skim” is a matrix generated by the model that represents the zone-to-zone travel distances and travel times for the entire model area.

Daily VMT by zone is calculated by multiplying the origin-destination trip tables by travel distance “skims” from the highway assignment for each time periods separately. Each time period is calculated separately because travel paths between origin and destination (and the resulting VMT) can vary due in congested versus uncongested travel conditions. The time periods are summed together to calculate the daily VMT. VMT is calculated separately to autos and trucks to facilitate air quality analysis which typically need this breakdown.

Zone-based VMT is calculated for four different categories:
• Travel wholly within the jurisdiction boundary (Internal to Internal) • Trips originating within the jurisdiction but ending outside the jurisdiction (Internal to External) • Trips originating outside the jurisdiction and ending inside the jurisdiction (External to Internal) • Trips with origins and destinations outside the jurisdiction (External to External) For the purposes of a CAP the VMT associated with “External to External” trips are not attributable to the jurisdiction. By convention only 50% of the VMT associated with “Internal to External” and “External to Internal” trips are allocated to the jurisdiction itself and the other 50% allocated to the non-jurisdiction (or “external”) end on the trip. VMT trip matrices are generated for four separate sets of geographies, and daily VMT is summarized as follows:
• Regional (summarized in Table 2-18),
• Cities (summarized in Table 2-19).

As summarized in Table 2-18, approximately 20% of VMT within Ventura County is attributed to internal-tointernal trips, and 80% of VMT is associated with internal to external trips. A complete summary of air quality and trip performance data is summarized in Appendix D.

Table 2-18: SBTM Base Year Regional Trip VMT (in 1,000s) Matrix EXTERNAL INTERNAL (City of Santa Barbara)
External N/A 1,782
Internal (City of Santa Barbara) 1,777 433
Sum of Internal-to-Internal VMT (in 1,000s) 433
Half the Sum of Internal-to-External VMT (in 1,000s) 1,780 %Internal to Internal 20%
%Internal to External 80%
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Table 2-19: SBTM Base Year City Trip VMT Matrix
Santa
Barbara Goleta Santa Maria Lompoc Carpinteria Solvang Buelton GuadalupeUnincorporated Ventura County San Luis
Obispo County Total
Santa Barbara 433,141 244,795 269,152 74,801 104,272 27,807 44,938 12,433 543,791 381,837 73,055 2,210,022 Goleta 244,912 95,831 743,475 132,737 35,057 14,284 65,374 6,101 309,413 31,134 66,794 1,745,113 Santa Maria 271,463 743,486 977,570 53,014 11,123 32,645 25,711 61,899 698,308 10,502 550,040 3,435,761 Lompoc 75,097 132,805 52,773 132,967 3,403 13,055 25,161 2,194 181,002 1,103 67,195 686,755 Carpinteria 105,298 35,402 10,994 3,371 25,151 2,835 3,839 1,133 85,748 113,811 3,817 391,399 Solvang 27,987 14,350 32,591 13,089 2,863 6,231 5,779 1,477 59,016 306 17,519 181,205 Buelton 45,945 65,284 25,879 25,329 3,968 5,787 4,243 1,253 60,527 186 13,668 252,069 Guadalupe 12,525 6,121 62,008 2,201 1,150 1,488 1,249 8,656 18,103 5 4,252 117,758 Unincorporated 545,106 310,768 696,329 178,689 85,619 58,996 60,381 18,079 631,525 60,378 122,977 2,768,847 Ventura County 380,493 31,489 9,972 1,113 113,212 319 193 5 60,320 0 406,323 1,003,439 San Luis Obispo
County
73,637 66,067 545,163 66,918 3,893 17,501 13,616 4,248 122,394 248,395 0 1,161,830 Total 2,215,603 1,746,399 3,425,906 684,229 389,709 180,947 250,482 117,479 2,770,147 847,657 1,325,641 13,954,197 ATTACHMENT 1
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2.11.2 Link-Based VMT
Traditional VMT calculations are link-based and include all the links within an area. This includes trip generated within that area as well as through trips. An advantage of the link-based methodology compared to the Zonebased method is that VMT by “speed bin” an important input to air quality model, can be calculated.

The SBTM model calculates VMT by speed bin for five miles per hour speed increments for each time period separately then adds together for derive daily VMT by speed bin. Speed bin and summary VMT data is calculated in a spreadsheet tool developed for SBTM. Regional air quality summary data is included in Table 2-

20. Table 2-21 summarizes regional VMT by facility type. A complete summary of air quality and trip performance data is summarized in Appendix E.

Table 2-20: SBTM Base Year (2020) Regional Air Quality Summary by Speed Bin SPEED
RANGE

AM PEAK
PERIOD
6 AM TO
9 AM

LATE AM
PERIOD
9 AM TO
12 PM
LUNCH
PERIOD
12 PM
TO 2 PM
EARLY
PM
PERIOD
2 PM TO
4 PM

PM PEAK
PERIOD
4 PM TO
6 PM

EVENING
PERIOD
6 PM TO 8
PM
LATE

EVENING
PERIOD
8 PM TO 12
AM
NIGHT
PERIOD
9 AM TO
7 AM

AVERAGE
DAILY

PERCENT
OF
TRAVEL
0 - 5 MPH 0 0 0 282 0 0 0 0 282 0.0%
5 - 10 MPH 191 250 0 0 308 0 0 0 748 0.1%
10 - 15 MPH 86 0 0 0 544 0 0 0 630 0.0%
15 - 20 MPH 161 0 128 74 306 0 0 0 669 0.1%
20 - 25 MPH 248 97 320 282 156 120 0 0 1,222 0.1%
25 - 30 MPH 7,600 8,331 6,435 8,464 11,499 5,615 3,334 3,729 55,008 4.2% 30 - 35 MPH 5,987 5,784 4,415 4,378 13,263 3,421 2,278 1,635 41,162 3.2% 35 - 40 MPH 7,630 6,108 5,135 7,057 29,987 3,927 2,284 1,913 64,041 4.9% 40 - 45 MPH 70,531 82,192 65,059 70,641 88,411 50,748 31,378 29,545 488,504 37.5% 45 - 50 MPH 9,587 1,160 579 530 11,783 711 2,404 2,454 29,209 2.2% 50 - 55 MPH 9,734 1,951 1,538 1,750 9,227 1,296 892 739 27,127 2.1% 55 - 60 MPH 27,432 21,052 15,701 21,598 34,506 14,094 9,361 13,340 157,083 12.0% 60 - 65 MPH 55,134 73,685 58,348 68,690 51,386 53,643 20,781 40,158 421,826 32.3% 65 - 70 MPH 0 0 0 0 0 0 11,135 5,652 16,787 1.3%
70 - 75 MPH 0 0 0 0 0 0 0 0 0 0.0%
75 - 80 MPH 0 0 0 0 0 0 0 0 0 0.0%
VMT - Total 194,321 200,609 157,658 183,746 251,376 133,575 83,847 99,166 1,304,299 100.0% *Note: Total values may differ slightly from Table 2-27 due to rounding.

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Table 2-21: SBTM Base Year Regional Air Quality Summary by Facility Type SPEED
RANGE

AM PEAK
PERIOD
6 AM TO
9 AM

LATE AM
PERIOD
9 AM TO
12 PM
LUNCH
PERIOD
12 PM
TO 2 PM
EARLY
PM
PERIOD
2 PM TO
4 PM

PM PEAK
PERIOD
4 PM TO
6 PM

EVENING
PERIOD
6 PM TO 8
PM
LATE

EVENING
PERIOD
8 PM TO 12
AM
NIGHT
PERIOD
9 AM TO
7 AM

AVERAGE
DAILY

PERCENT
OF
TRAVEL
Freeway 99,288 94,683 74,004 90,238 121,756 67,703 41,255 59,128 648,055 49.7% Expressway 0 0 0 0 0 0 0 0 0 0.0%
Principal
Arterial
40,536 45,633 35,784 40,163 53,395 27,592 17,923 17,179 278,206 21.3% Minor Arterial 19,129 20,866 16,631 18,768 29,067 13,479 8,744 7,580 134,266 10.3% Urban
Collector
24,469 27,420 21,865 24,277 34,196 16,986 10,795 9,989 169,998 13.0% Rural Collector 0 0 0 0 0 0 0 0 0 0.0%
Local Roads 3,163 3,688 2,888 3,297 4,515 2,244 1,518 1,232 22,545 1.7% Ramps 7,735 8,319 6,486 7,002 8,448 5,571 3,611 4,057 51,229 3.9% System Ramp
to Ramp
Connector
0 0 0 0 0 0 0 0 0 0.0%
HOV 0 0 0 0 0 0 0 0 0 0.0%
VMT - Total 194,321 200,609 157,658 183,746 251,376 133,575 83,847 99,166 1,304,299 100.0% *Note: Total values may differ slightly from Table 2-26 due to rounding.

2.11.3 SB 743 VMT
Zonal-based VMT for home-based and work-based trips are summarized by City and used for SB 743 analysis. The VCTC model calculates the required model outputs in a spreadsheet tool developed for the updated SBTM. SB 743 VMT data is included in Table 2-22, and a complete summary of SB 743 VMT data is summarized in Appendix E.

Table 2-22: SBTM Base Year SB 743 VMT
City Home-based VMT
Per Capita
Work-based VMT
Per Employee
Total VMT
Per Service Population
(Capita + Employee)
Santa Barbara 10.43 24.32 30.67
Overall Santa Barbara County 18.75 24.56 44.41
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3 MODEL CALIBRATION AND VALIDATION
The benefits of using the prototype model developed by SBCAG is that the SBTM retains the regional coefficients and parameters. These coefficients and parameters were validated specifically for the City of Santa Barbara. Due to several seasonal variations in traffic in Santa Barbara from tourists and resident vacations, the model was calibrated and validated to average mid-week traffic. School breaks, weather, and other major disruptions were avoided during the model calibration process. This allows for accurate representation of normal day-to-day activities of the City.

The data collection process involved data sources such as SBCAG, Caltrans, the City of Santa Barbara traffic counts, land use, and roadway network data.

3.1 Model Calibration
Several model parameters and inputs were calibrated specifically to meet the needs of City of Santa Barbara model efforts. The calibrated model information is included in this section of the report.

3.1.1 Trip Generation
The trip production and attraction values for the SBTM were compared with the SBCAG model estimates. As detailed in Table 3-1, the SBTAM trip generation estimates over 10% greater trip productions and attractions than the SBTM for the City of Santa Barbara zones. The primary reason for this difference is the verification and modification of land use within the City.

Table 3-1: Trip Generation Validation
Trip Purpose
SBTM 2020 Model SBCAG 2020 Model Difference
Trip
Productions
Trip
Attractions
Trip
Productions
Trip
Attractions
Trip
Productions
Trip
Attractions
Home-Based Work (HBW) 92,413 125,541 102,986 140,475 90% 89% Home-Based Shopping (HBShop) 37,587 32,095 39,427 40,512 95% 79% Home-Based School (HBSch) 40,355 20,609 50,165 22,899 80% 90% Home-Based Other (HBO) 165,848 219,888 180,864 265,818 92% 83% Non-Home-Based Work (NHBW) 58,343 53,433 63,603 63,949 92% 84% Non-Home-Based Other (NHBO) 72,027 87,776 78,303 108,685 92% 81% Total 466,574 539,342 515,348 642,338 91% 84%

3.1.2 Trip Distribution
Trip distribution procedures in the SBTM are identical with the trip distribution as documented in the SBCAG Model documentation, summarized in Appendix A. Table 3-2 illustrates the trip length validation for the SBTM as compared to the SBCAG model. As illustrated, there are some differences ranging up to 20%. The primary reason for this difference is the verification and modification of land use within the City.

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Table 3-2: Trip Distribution Validation
Trip Purpose
SBCAG 2020 Model
City of Santa Barbara
SBTM 2020 Model
City of Santa Barbara Difference
Average
Distance (Miles)
Average Time
(Minutes)
Average
Distance (Miles)
Average Time
(Minutes)
Average
Distance (Miles)
Average Time
(Minutes)
Home-Based Work
(HBW) 7.9 18.8 7.3 17.8 92% 95%
Home-Based
Shopping (HBShop) 7.9 18.6 7.8 18.2 99% 98%
Home-Based School
(HBSch) 7.9 18.9 7.5 18.1 96% 96%
Home-Based Other
(HBO) 8.0 18.9 7.3 17.8 91% 94%
Non-Home-Based
Work (NHBW) 6.9 17.6 5.6 15.8 80% 90%
Non-Home-Based
Other (NHBO) 86.3 101.5 102.7 122.5 119% 121%

3.1.3 Mode Choice
The 2012 California Household Travel survey provided mode share of auto and non-motorized trips for multiple trip purposes, time periods, and household income markets. Mode choice procedures in the SBTM are largely consistent with mode choice as documented in the 2010 SBCAG Model documentation, summarized in Appendix A. Table 3-3 and Table 3-4 summarize the calibration of the mode choice model in the SBTM by peak and off-peak, respectively.

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Table 3-3: 2020 SBTM Peak Mode Choice by Trip Purpose Summary Statistics (Santa Barbara County) Observed Peak Mode Shares by Trip Purpose 2020 SBTM Peak Mode Shares by Trip Purpose Observed vs. SBTM Mode Shares Difference HBW HBShop HBO HBSch NHBW NHBO HBW HBShop HBO HBSch NHBW NHBO HBW HBShop HBO HBSch NHBW NHBO DA 85% 57% 30% 9% 1% 28% 85% 55% 30% 9% 1% 26% 0% -3% 0% 3% 2% -5% CP 8% 42% 65% 60% 1% 71% 8% 42% 65% 60% 2% 72% 0% 0% 0% 0% 16% 1% Bus 1% 0% 0% 3% 79% 0% 1% 0% 0% 3% 79% 0% 33% 17% -2% 0% 115% Walk 4% 2% 4% 6% 19% 1% 4% 4% 4% 5% 18% 1% 4% 108% 5% -3% 0% 22% Bike 2% 0% 1% 10% 0% 0% 2% 0% 1% 9% 0% 0% -1% 2% -1% 35% School Bus 0% 0% 0% 13% 0% 0% 0% 0% 0% 13% 0% 0% 2%
Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 0% 0% 0% 0% 0% 0% Table 3-4: 2020 SBTM Off-Peak Mode Choice by Trip Purpose Summary Statistics (Santa Barbara County) Observed Off-Peak Mode Shares by Trip Purpose 2020 SBTM Off-Peak Mode Shares by Trip Purpose Observed vs. SBTM Mode Shares Difference HBW HBShop HBO HBSch NHBW NHBO HBW HBShop HBO HBSch NHBW NHBO HBW HBShop HBO HBSch NHBW NHBO DA 85% 57% 40% 27% 1% 39% 84% 55% 40% 27% 1% 36% -1% -4% 0% 1% -1% -7% CP 10% 38% 53% 34% 4% 56% 10% 37% 53% 34% 4% 57% -1% -1% 0% -1% 13% 3% Bus 0% 2% 0% 2% 82% 1% 0% 2% 1% 1% 82% 3% 0% -4% 3% -3% -1% 84% Walk 3% 2% 5% 5% 13% 3% 3% 5% 5% 5% 13% 3% 15% 120% 4% -1% -1% 2% Bike 2% 1% 2% 18% 0% 0% 3% 1% 2% 18% 0% 0% 14% -5% 2% 1% 19% School Bus 0% 0% 0% 14% 0% 0% 0% 0% 0% 14% 0% 0% 0%
Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 0% 0% 0% 0% 0% 0% ATTACHMENT 1
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3.2 Assignment Validation
The assignment process is the last and most iterative part of validation of a travel demand model. To validate the model outputs, base-year daily count data were obtained throughout the City of Santa Barbara to compare the observed versus estimated traffic volumes on the highway network. The primary source for freeway mainlines and state highway traffic volume data was obtained from Caltrans annual traffic counts.

This section of the report documents the assignment validation for the SBTM. Details related to the assignment validation are summarized in Appendix G.

3.2.1 ADT 2020 Growth
As the base year of the model is assumed to be 2020, the count data used to validate the model is required to be a year 2020 count. The most recent years counts were collected in 2015 and 2019, which means that a growth factor is required to calculate a representative ADT for the year 2020. It should be noted that counts collected in 2020 would not be a true representation of actual travel, as traffic in 2020 was greatly reduced and impacted by COVID-19 restrictions, thus an estimated 2020 ADT is a more conservative and realistic approach to model validation in the year 2020.

3.2.2 Screenline Comparison
Screenlines are imaginary lines drawn across sections of roadways to assess the overall performance of the model by comparing the total model assigned volumes and total actual counts for specific screenline locations.

All model links (segments) cross a screenline form a group of roadways within a corridor for which the total model produced volumes and ground traffic counts are aggregated and compared. Sixteen screenlines (illustrated in Figure 3-1) were identified across different locations in the CIty:
• 1. North-South: East of US-101 between Las Positas Road and Haley Street • 2. North-South: West of US-101 between Las Positas Road and Haley Street • 3. East-West: South of State Street between US-101 and Alamar Avenue • 4. North-South: East of Milpas Street between Arbolado Road and Cabrillo Boulevard • 5. North-South: East of Salinas Street between Cabrillo Boulevard and Mountain Road • 6. North-South: East of State Street between Constance Avenue and Shoreline Drive • 7. East-West: North of Anapamu Street between Gillespie Street and Milpas Street • 8. East-West: Between Modoc Road and State Street
• 9. North-South: North of Shoreline Drive between Loma Alta Drive and Channel Drive • 10. East-West: North of Gutierrez St between Bath Street and Alameda Padre Serra • 11. North-South: West of De La Vina Street between Vernon Road and Shoreline Drive • 12. East-West: North of Mission Street between Modoc Road and Alameda Padre Serra • 13. Cutline: Cliff Drive West of Meigs Road
• 101. Cutline: US-101 (Western Santa Barbara City Border) • 102. Cutline: SR-154 (Northern Santa Barbara City Border) • 103. Cutline: PCH and SR-192 (Eastern Santa Barbara City Border) Individual link volumes were summed from both the model and the ground counts across all screenlines. Table 3-5 summarizes the existing counts and assigned traffic volume flows across each screenline, and also the ATTACHMENT 1
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percentage difference between the two. As shown in the table, all screenlines have the assigned volumes within the acceptable industry validation standards. Figure 3-2 illustrates the maximum desirable deviation in total screenline volumes as recommended by NCHRP 255 Report and FHWA. The indicated curve is the desirable maximum percent deviation for total screenline volumes from counts, given the total volumes for a screenline.

As shown in Figure 3-2, all the screenlines except one fall within the acceptable tolerance levels established by FHWA.

Figure 3-1: SBTM Screenline Locations
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Table 3-5: SBTM Screenline Validation
# NAME DIRECTION
NUMBER
OF
COUNTS
TOTAL
SUM OF
COUNTS

TOTAL VOLUME

AT LOCATIONS WITH
COUNTS

VOLUME/
COUNT
RATIO
PASS/
FAIL

CRITERIA
1 East of US-101 between Las Positas Rd
and Haley St NS 6 127,730 135,179 5.8% PASS
2 West of US-101 between Las Positas Rd
and Haley St NS 4 63,080 45,847 -27.3% PASS
3 South of State St between US-101 and
Alamar Ave EW 7 53,200 45,491 -14.5% PASS
4 East of Milpas St between Arbolado Rd
and Cabrillo Blvd NS 6 22,535 13,236 -41.3% PASS
5 East of Salinas St between Cabrillo Blvd
and Mountain Rd NS 4 57,030 53,018 -7.0% PASS
6 East of State St between Constance Ave
and Shoreline Dr NS 6 140,310 129,039 -8.0% PASS
7 North of Anapamu Street between
Gillespie St and Milpas St EW 10 88,000 74,115 -15.8% PASS 8 Between Modoc Rd and State St EW 1 16,270 16,935 4.1% PASS 9 North of Shoreline Dr between Loma
Alta Dr and Channel Dr NS 2 15,410 13,755 -10.7% PASS
10 North of Gutierrez St between Bath St
and Alameda Padre Serra EW 3 40,200 35,503 -11.7% PASS
11 West of De La Vina St between Vernon
Rd and Shoreline Dr NS 3 48,120 58,109 20.8% PASS
12 North of Mission St between Modoc Rd
and Alameda Padre Serra EW 6 47,520 48,401 1.9% PASS
13 Cutline on Cliff Drive West of Meigs Rd NS 1 17,190 10,648 -38.1% PASS 101 Cutline on US-101 (Western Santa
Barbara City Border) NS 4 146,000 137,845 -5.6% PASS
102 Cutline on SR-154 (Northern Santa
Barbara City Border) EW 3 24,500 20,507 -16.3% PASS
103 Cutline on PCH and SR-192 (Eastern
Santa Barbara City Border) EW 2 70,100 83,058 18.5% PASS Combined Screenline Totals 64 977,195 920,686 -5.8%
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Figure 3-2: SBTM Screenline Verification (Maximum Desirable Deviation)

3.2.3 Regional Comparison
A regional comparison comprised of observed ground counts and modeled volumes is used to determine the validity of the traffic assignment across all points and facility types in the model. Table 3-6 summarizes the overall difference between the observed counts and model results by facility type. Caltrans and FHWA provide guidance on the levels of errors that should be accepted in these validation metrics. The guidance in overall deviation in modeled volumes compared to traffic counts is summarized in Table 3-6.

Table 3-6: Count/Volume Comparison By Facility Type
Caltrans and FHWA Criteria: PASS/FAIL CRITERIA
Freeways +/- 7% -1%
Major Arterials +/- 10% 10%
Minor Arterials +/- 15% -10%
Note:

Caltrans Criteria is from the Caltrans Travel Forecasting Guidelines, November 1992.

FHWA Criteria was obtained from the Model Validation and Reasonableness Checking Manual.

Figure 3-11 compares the observed volumes to the daily model output volumes for all roadway and highway segments with available counts. Figure 3-11 illustrates that the count to volume comparison is generally clustered along a 45º fit-line which shows good correlation between actual and observed volumes.

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Figure 3-3: SBTM Count versus Volume Comparison

3.2.4 Coefficient of Determination (R2
)
Another important measure of comparison for the fit between observed traffic counts and estimated model volumes is the Coefficient of Determination (R2). The FHWA recommended value of R2 is greater than 0.88 (Model Validation and Reasonableness Checking Manual, Travel Model Improvement Program). The computed overall value of R2 is 0.89 for the SBTM, which shows that the model’s performance is well within a reasonable range, representing an excellent fit.

3.2.5 Root Mean Square Error (RMSE)
RMSE is a quadratic scoring rule which measures the average magnitude of the error between observed traffic counts and model predicted volumes. The equation for RMSE is as follows:
%𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = �
∑ ( 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖 − 𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖)2 (𝑁𝑁 𝑁𝑁𝑁𝑁 𝑁𝑁 𝑜𝑜 𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 − 1)0.5 ⁄ ∗ 100 (∑ 𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖⁄𝑁𝑁 𝑁𝑁𝑁𝑁 𝑁𝑁 𝑜𝑜 𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶) �
Both Caltrans and FHWA (through the Travel Model Improvement Program (TIMP) Model Validation and Reasonableness Checking Manual) have recommended guidance for regional RMSE. Caltrans recommended guidance suggests a %RMSE less than 40% and FHWA recommended guidance suggests a %RMSE less than 30%.

The validated model %RMSE is 32, which passes the Caltrans recommended guidance.

3.3 Conclusion
The results of model validation/calibration process for the SBTM, based on the detailed review of each individual module, indicate a favorable validation for 2020 base year conditions. Traffic assignment summary statistics for the focused model are well within acceptable industry standards. In addition, assignment volumes summed along critical screenlines and travel corridors closely replicate actual ground counts based on established industry standards. It is therefore concluded that the model is ready to be used to develop forecast traffic forecasts assignments for use in various studies.

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4 FORECAST YEAR MODEL
This section of the report documents the 2035 forecast year model assumptions and results. The 2035 model input files are identical to the validated model files with the exception of the land use and socioeconomic data and network inputs.

4.1 Transportation Analysis Zones
The zone structure of the 2035 forecast year is identical to the zone structure of the base year, with only data inputs being modified.

4.1.1 Socioeconomic Data
The land use data for each TAZ is converted to three (3) major socioeconomic variables (population, households and employment) and further disaggregated into secondary variables (e.g. household size, age, income level, employment type, etc.).

Table 4-1 summarizes the SBTM forecasts with the SBCAG 2035 baseline scenario forecasts. As shown in Table 4-1, the SBTM closely resembles the SBCAG 2035 baseline scenario forecasts. Table 4-2 summarizes the 2020 and 2035 population, households, and employment totals for the City of Santa Barbara. As summarized in Table 4-2, the updated SBTM assumes an approximate population growth of 24%, household growth of 13%, and employment growth of 7%.

Table 4-1: SBTM Population, Households, and Employment Compared to SBCAG SOCIOECONOMIC VARIABLE SBCAG 2035 BASELINE SBTM 2035 GROWTH % Population 129,793 138,569 6.8%
Households 51,866 53,324 2.8%
Employment 92,727 84,632 -8.7%
Table 4-2: SBTM Population, Households, and Employment
SOCIOECONOMIC VARIABLE 2020 2035 GROWTH %
Population 111,615 138,569 24.1%
Households 47,315 53,324 12.7%
Employment 79,157 84,632 6.9%

4.2 Networks
Minimal updates were made to the 2035 network obtained from SBCAG. Modifications primarily included a thorough review of speeds on streets in the City of Santa Barbara, along with a review of one-way streets in the downtown area. Occasionally, some local streets were added to the network to increase the density of roadway network in some larger TAZs.

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4.2.1 Forecast Year Baseline Improvements
Forecast year 2035 Baseline projects included in the future year baseline network are:
• CT-11: A roundabout at Olive Mill Road and Coast Village Road, with construction completed in fall 2023
• CT-PL-1: US-101 HOV lanes between Hot Springs Road and connecting east of Santa Barbara County to the existing US-101 HOV lanes, programmed for 2027
• SB-PL-3: A roundabout at Las Patos Way and East Cabrillo Boulevard, programmed for 2025

4.2.2 Transit Networks
Within the City of Santa Barbara, there are multiple transit authorities, and modes of transit. Table 4-3 summarizes the route information within the forecast year SBTM. No update was made to future transit network as a part of this update.

Table 4-3: Forecast Year Transit Route Summary

TRANSIT AUTHORITY NUMBER OF ROUTES
Amtrak 2
Clean Air Express 5
Greyhound 1
Santa Barbara MTD 27
Santa Barbara Airbus 1
Ventura County Transportation
Commission (VCTC) Coastal Express 1

4.3 Mode Choice
Table 4-4 and Table 4-5 illustrate comparisons of the updated 2020 base year model with the 2035 forecast year model mode choice results for the SBTM. Table 4-4 summarizes mode choice by trip purpose, and Table 4- 5 summarizes trip purpose by time of day.

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Table 4-4: 2035 SBTM Peak Mode Choice by Trip Purpose Summary Statistics (Santa Barbara County) 2020 SBTM Peak Mode Shares by Trip Purpose 2035 SBTM Peak Mode Shares by Trip Purpose 2020 vs. 2035 Mode Shares Difference HBW HBShop HBO HBSch NHBW NHBO HBW HBShop HBO HBSch NHBW NHBO HBW HBShop HBO HBSch NHBW NHBO DA 85% 57% 30% 9% 1% 28% 85% 55% 30% 9% 1% 26% 0% -3% 0% 3% 2% -5% CP 8% 42% 65% 60% 1% 71% 8% 42% 65% 60% 2% 72% 0% 0% 0% 0% 16% 1% Bus 1% 0% 0% 3% 79% 0% 1% 0% 0% 3% 79% 0% 33% 17% -2% 0% 115% Walk 4% 2% 4% 6% 19% 1% 4% 4% 4% 5% 18% 1% 4% 108% 5% -3% 0% 22% Bike 2% 0% 1% 10% 0% 0% 2% 0% 1% 9% 0% 0% -1% 2% -1% 35% School Bus 0% 0% 0% 13% 0% 0% 0% 0% 0% 13% 0% 0% 2%
Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 0% 0% 0% 0% 0% 0% Table 4-5: 2035 SBTM Off-Peak Mode Choice by Trip Purpose Summary Statistics (Santa Barbara County) 2020 SBTM Off-Peak Mode Shares by Trip Purpose 2035 SBTM Off-Peak Mode Shares by Trip Purpose 2020 vs. 2035 Mode Shares Difference HBW HBShop HBO HBSch NHBW NHBO HBW HBShop HBO HBSch NHBW NHBO HBW HBShop HBO HBSch NHBW NHBO DA 85% 57% 40% 27% 1% 39% 84% 55% 40% 27% 1% 36% -1% -4% 0% 1% -1% -7% CP 10% 38% 53% 34% 4% 56% 10% 37% 53% 34% 4% 57% -1% -1% 0% -1% 13% 3% Bus 0% 2% 0% 2% 82% 1% 0% 2% 1% 1% 82% 3% 0% -4% 3% -3% -1% 84% Walk 3% 2% 5% 5% 13% 3% 3% 5% 5% 5% 13% 3% 15% 120% 4% -1% -1% 2% Bike 2% 1% 2% 18% 0% 0% 3% 1% 2% 18% 0% 0% 14% -5% 2% 1% 19% School Bus 0% 0% 0% 14% 0% 0% 0% 0% 0% 14% 0% 0% 0%
Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 0% 0% 0% 0% 0% 0% ATTACHMENT 1
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4.4 Air Quality Data Module
Consistent with the discussion on the air quality data module in Section 2.11 of this report, forecast year air quality performance has been developed for zonal based VMT as well as link based VMT.

4.4.1 Zone-Based VMT
Daily VMT by zone is calculated by multiplying the origin-destination trip tables by travel distance “skims” from the highway assignment for each time periods separately. Each time period is calculated separately because travel paths between origin and destination (and the resulting VMT) can vary in congested versus uncongested travel conditions. VMT trip matrices are generated for two separate sets of geographies, and daily.

VMT is summarized as follows:
• Regional (summarized in Table 4-6),
• Cities (summarized in Table 4-7).

As summarized in Table 4-9, approximately 19% of VMT within the City is attributed to internal-to-internal trips, and 81% of VMT is associated with internal to external trips. A complete summary of air quality and trip performance data is summarized in Appendix D.

Table 4-6: SBTM Forecast Year Regional Trip VMT (in 1,000s) Matrix

EXTERNAL INTERNAL (SANTA BARBARA CITY)
External N/A 2,334
Internal (Santa Barbara City) 2,321 529
Sum of Internal-to-Internal VMT (in 1,000s) 529
Sum of Internal-to-External VMT (in 1,000s) 2,328
%Internal to Internal 19%
%Internal to External 81%
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Table 4-7: SBTM Forecast Year City Trip VMT Matrix
Santa
Barbara Goleta Santa Maria Lompoc Carpinteria Solvang BuelltonGuadalupeUnincorporated Ventura County San Luis
Obispo
County
Total
Santa Barbara 528,537 274,761 388,421 70,551 111,192 26,153 44,511 13,569 581,546 743,388 66,812 2,849,441 Goleta 274,743 105,111 612,827 139,757 36,749 10,562 61,671 6,439 290,363 67,900 85,806 1,691,927 Santa Maria 399,313 614,522 1,226,130 57,702 16,646 34,683 25,965 73,520 811,673 16,524 601,101 3,877,779 Lompoc 71,329 139,821 57,572 143,035 3,992 13,735 28,336 2,223 183,495 1,808 74,274 719,620 Carpinteria 112,323 37,173 16,452 3,931 27,917 2,964 4,551 1,268 95,264 231,628 4,309 537,780 Solvang 26,407 10,575 34,598 13,762 2,997 6,454 5,847 1,589 68,038 523 18,895 189,685 Buellton 44,645 61,638 26,108 28,508 4,563 5,843 5,246 1,207 67,971 318 15,489 261,535 Guadalupe 13,852 6,529 73,654 2,226 1,294 1,605 1,205 9,865 19,534 7 4,580 134,352 Unincorporated 582,361 291,711 808,341 180,819 95,081 67,983 67,939 19,523 702,168 117,137 128,172 3,061,236 Ventura County 741,979 68,525 15,726 1,841 231,149 549 331 8 117,066 0 534,918 1,712,093 San Luis Obispo
County 67,472 84,901 595,581 73,890 4,411 18,855 15,432 4,578 127,573 327,008 0 1,319,702 Total 2,862,961 1,695,266 3,855,411 716,021 535,990 189,386 261,035 133,788 3,064,693 1,506,242 1,534,357 16,355,150 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 36

4.4.2 Link-Based VMT
Traditional VMT calculations are link-based and include all the links within an area. This includes trips generated within that area as well as through trips. An advantage of the link-based methodology compared to the Zonebased method is that VMT by “speed bin” an important input to air quality model, can be calculated.

The VCTC model calculates VMT by speed bin for five miles per hour speed increments for each time period separately then adds together for derive daily VMT by speed bin. Speed bin and summary VMT data is calculated in a spreadsheet tool developed for SBTM. Regional air quality summary data is included in Table 4-8. Table 4-9 summarizes regional VMT by facility type. Table 4-10 summarizes growth in VMT between the SBTM base year and forecast year models. A complete summary of air quality and trip performance data is summarized in Appendix E of the full report.

Table 4-8: SBTM Forecast Year Regional Air Quality Summary by Speed Bin SPEED
RANGE

AM PEAK
PERIOD
6 AM TO
9 AM

LATE AM
PERIOD
9 AM TO
12 PM
LUNCH
PERIOD
12 PM
TO 2 PM
EARLY
PM
PERIOD
2 PM TO
4 PM

PM PEAK
PERIOD
4 PM TO
6 PM

EVENING
PERIOD
6 PM TO 8
PM
LATE

EVENING
PERIOD
8 PM TO
12 AM
NIGHT
PERIOD
9 AM TO
7 AM

AVERAGE
DAILY

PERCENT
OF
TRAVEL
0 - 5 MPH 0 0 0 292 289 0 0 0 581 0.0%
5 - 10 MPH 194 285 0 0 39 0 0 0 517 0.0%
10 - 15 MPH 178 0 0 0 613 0 0 0 791 0.1%
15 - 20 MPH 103 0 177 2,176 2,320 127 0 0 4,904 0.3%
20 - 25 MPH 687 161 409 360 2,627 262 0 0 4,506 0.3%
25 - 30 MPH 9,494 10,430 7,906 8,300 16,534 6,785 4,500 4,746 68,694 4.4% 30 - 35 MPH 7,167 6,648 4,943 5,229 23,193 3,852 2,415 1,835 55,281 3.5% 35 - 40 MPH 13,691 8,093 8,080 9,881 54,224 5,326 3,066 2,669 105,030 6.7% 40 - 45 MPH 89,467 96,773 75,128 83,309 85,844 59,941 38,665 35,636 564,762 35.8% 45 - 50 MPH 3,034 1,051 461 2,608 24,099 551 2,317 2,215 36,336 2.3% 50 - 55 MPH 16,133 2,284 1,782 5,707 18,284 1,468 1,038 841 47,537 3.0% 55 - 60 MPH 38,645 29,062 21,642 43,895 32,325 18,949 13,969 18,970 217,456 13.8% 60 - 65 MPH 51,482 88,213 68,971 60,874 37,880 65,202 31,571 51,953 456,145 28.9% 65 - 70 MPH 0 0 0 0 0 0 9,123 4,273 13,396 0.9%
70 - 75 MPH 0 0 0 0 0 0 0 0 0 0.0%
75 - 80 MPH 0 0 0 0 0 0 0 0 0 0.0%
VMT - Total 230,274 242,998 189,499 222,630 298,271 162,462 106,665 123,137 1,575,936 100.0% ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 37
Table 4-9: SBTM Forecast Year Regional Air Quality Summary by Facility Type SPEED
RANGE

AM PEAK
PERIOD
6 AM TO
9 AM

LATE AM
PERIOD
9 AM TO
12 PM
LUNCH
PERIOD
12 PM
TO 2 PM
EARLY
PM
PERIOD
2 PM TO
4 PM

PM PEAK
PERIOD
4 PM TO
6 PM

EVENING
PERIOD
6 PM TO 8
PM
LATE

EVENING
PERIOD
8 PM TO
12 AM
NIGHT
PERIOD
9 AM TO
7 AM

AVERAGE
DAILY

PERCENT
OF
TRAVEL
Freeway 112,745 114,772 88,583 108,296 137,276 83,238 54,018 74,535 773,465 49.1% Expressway 0 0 0 0 0 0 0 0 0 0.0%
Principal
Arterial 48,323 52,802 40,950 47,026 63,906 31,950 21,578 19,976 326,511 20.7% Minor Arterial 23,638 25,288 20,176 23,136 36,235 16,418 10,586 9,222 164,700 10.5% Urban
Collector 29,831 33,196 26,555 29,686 42,036 20,640 13,335 12,255 207,535 13.2% Rural Collector 0 0 0 0 0 0 0 0 0 0.0%
Local Roads 3,793 4,400 3,431 3,957 6,471 2,660 1,802 1,447 27,962 1.8% Ramps 9,058 10,115 7,836 8,273 9,358 6,687 4,731 5,067 61,127 3.9% System Ramp
to Ramp
Connector
0 0 0 0 0 0 0 0 0 0.0%
HOV 2,887 2,425 1,968 2,254 2,988 869 614 633 14,638 0.9% VMT - Total 230,274 242,998 189,499 222,630 298,271 162,462 106,665 123,137 1,575,936 100.0% Table 4-10: SBTM Base vs. Forecast Year VMT Growth
SPEED
RANGE

AM PEAK
PERIOD
6 AM TO
9 AM

LATE AM
PERIOD
9 AM TO
12 PM
LUNCH
PERIOD
12 PM TO
2 PM

EARLY PM
PERIOD
2 PM TO 4
PM

PM PEAK
PERIOD
4 PM TO 6
PM

EVENING
PERIOD
6 PM TO 8
PM
LATE

EVENING
PERIOD
8 PM TO 12
AM
NIGHT
PERIOD
9 AM TO
7 AM

AVERAGE
DAILY
2020
(Base Year)
194,321 200,609 157,658 183,746 251,376 133,575 83,847 99,166 1,304,299 2035
(Forecast year)
230,274 242,998 189,499 222,630 298,271 162,462 106,665 123,137 1,575,936 Growth % 19% 21% 20% 21% 19% 22% 27% 24% 21%

4.4.3 SB 743 VMT
Zonal-based VMT for home-based and work-based trips are summarized by City and used for SB 743 analysis.

The VCTC model calculates the required model outputs in a spreadsheet tool developed for the updated SBTM. SB 743 VMT data is included in Table 4-11, and a complete summary of SB 743 VMT data is summarized in Appendix F.

ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 38
Table 4-11: SBTM Forecast Year SB 743 VMT
City Home-based VMT
Per Capita
Work-based VMT
Per Employee
Total VMT
Per Service Population
(Capita + Employee)
Santa Barbara 10.90 24.97 31.92
Overall Santa Barbara County 19.43 24.22 44.99
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 39
5 USER’S GUIDE
This user’s guide is intended to assist in understanding how to run the travel demand model. The SBTM model is a custom Add-in in TransCAD software. Figure 5-1 illustrates the main add-in component of the travel model, which will be discussed in detail in the following sections.

Figure 5-1: Model Add-in Component
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 40

5.1 Installing the SBTM
Note: The SBTM requires TransCAD Version 9.0 Build 32735 64-bit build dated November 10, 2021 or newer.

Please contact Caliper to download this version.

The SBTM add-in is packaged in an easy-to-install package. In order to install the SBTM, all files in the SBTM_Model_Install_Files.zip folder need to be unzipped the C:\PROJECTS\SantaBarbaraModel file on the machine which has the correct version of TransCAD installed.

5.2 Defining Scenarios in the SBTM
Defining a scenario in the SBTM prior to a model run requires a few steps:
- The sbcity.scenarios file is located in the model folder (C:\PROJECTS\SantaBarbaraModel\SBCITY_Model_GP2035), and can be edited for project scenarios as required. The file is set up with parameters to run the SBTM for the 2020 and 2035 Baseline scenarios.
- As discussed earlier in the report, the master network will need to be updated to include required projects for the scenario model run. One piece of this component is updating the projects to be included within the network, these projects are identified in the C:\PROJECTS\11487_SantaBarbaraModelDev\SBCITY_Model_GP2035\fixeddata\Common\ RTP_Project_Database.bin. This file is a .bin file which contains a list of project ID’s and which scenarios they are to be included in.
o The master network RTP_ID_1, RTP_ID_2, and RTP_ID_3 attributes need to be updated with the project ID and project attributes as necessary.
o As a note, the scenario name in this database matches the scenario name in the sbcity.scenarios file.
o In addition to the highway network, the transit network may also be updated for RTP_IDs to be included in scenarios as necessary.

5.3 Launching the SBTM
Following installation of the SBTM add-in, the add-in can be opened through TransCAD in one of two ways:
- Drag and drop the C:\PROJECTS\SantaBarbaraModel\SBCITY_Model_GP2035\sbcity.model file into TransCAD.
- Open the C:\PROJECTS\SantaBarbaraModel\SBCITY_Model_GP2035\sbcity.model file by going to File-> Open.

In order to run a full model, the following simple steps must be followed (illustrated in Figure 5-4):

1. In the drop-down list, pick the desired scenario from the sbcity.scenarios file.

2. Right-click on the Land Use button, and select Run -> Run Model from Here ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 41
Figure 5-2: SBTM Model Run Setup
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | 42

5.4 Select Link Analysis
The SBTM model is capable of performing select link runs. To define if a select link analysis is required in a model run, two steps are required:

1. Look in the sbcity.scenarios file, and ensure that the [Critical Link Flag] = 1 for the desired scenario.

2. Look into the project scenario Inputs folder and update the CriticalLinkQueryFile.qry file using the TransCAD Planning -> Assignment Utilities -> Select Link/Zone Query Builder.

ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix
APPENDIX A – SBCAG TRAVEL MODEL DOCUMENTATION
ATTACHMENT 1
1 SBCAG Travel Model Development
Final Report
Prepared by
Caliper Corporation
1172 Beacon St. 3rd Floor
Newton, MA 02461
September, 2021
ATTACHMENT 1
2 Contents
Executive Summary ......................................................................................................................... 7 SBCAG Regional Travel Model Description ................................................................................... 11 TAZ Development...................................................................................................................... 11 Demographics............................................................................................................................ 13 Network Development.............................................................................................................. 17 Master Network Extraction and Highway Network Creation.................................................... 22 Transit Geographic Database ................................................................................................ 23 Transit Network Creation .......................................................................................................... 25 Highway and Transit Skims........................................................................................................ 27 Trip Generation ......................................................................................................................... 29 Generation of 4D variables.................................................................................................... 32 Model Estimation Results...................................................................................................... 33 Trip Production Models......................................................................................................... 42 Results ................................................................................................................................... 50 Trip Attraction Models .......................................................................................................... 51 School Trips ........................................................................................................................... 61 Results ................................................................................................................................... 61 Special Generators ................................................................................................................ 63 Internal‐External (IX) and External‐Internal (XI) Productions and Attractions...................... 65 Visitor Productions and Attractions...................................................................................... 69 Trip Distribution ........................................................................................................................ 73 Destination Choice Model ..................................................................................................... 76 Destination Choice Model Application.................................................................................. 79 Mode choice.............................................................................................................................. 81 Time of Day................................................................................................................................ 95 Truck Model............................................................................................................................... 97 Highway and Transit Trip Assignment..................................................................................... 100 Feedback.................................................................................................................................. 107 Sensitivity Analysis .................................................................................................................. 109 Future Year Models................................................................................................................. 112 ATTACHMENT 1
3 Trip Generation ................................................................................................................... 113 Trip Distribution .................................................................................................................. 113 Mode Choice........................................................................................................................ 114 Time of Day.......................................................................................................................... 115 Truck Trips........................................................................................................................... 117 Assignment.......................................................................................................................... 117 ATTACHMENT 1
4 List of Tables
Table 1 TAZ Numbering Scheme ................................................................................................... 12 Table 2 Number of Model TAZs..................................................................................................... 12 Table 3 TAZ Variables .................................................................................................................... 16 Table 4 Network Nodes and Links................................................................................................. 17 Table 5 Network Attributesetwork Attributes.............................................................................. 18 Table 6 Node Network Attributes ................................................................................................. 20 Table 7 Route Layer Attributes...................................................................................................... 23 Table 8 Route Stop Layer Attributes ............................................................................................. 24 Table 9 Transit Best Path Fields..................................................................................................... 28 Table 10 Transit Skim Tables......................................................................................................... 28 Table 11 Population Synthesis Household Summary.................................................................... 30 Table 12 Population Synthesis Person Summary .......................................................................... 30 Table 13 Auto Ownership Variables.............................................................................................. 32 Table 14 4Ds Variables .................................................................................................................. 33 Table 15 Auto Ownership Model vs. ACS PUMS Comparison....................................................... 42 Table 16: HBW Production Trip Rates........................................................................................... 43 Table 17: HBShop Production Trip Rates....................................................................................... 44 Table 18: HBSchool Production Trip Rates.................................................................................... 45 Table 19: HBOther Production Trip Rates..................................................................................... 46 Table 20: NHBW Production Trip Rates......................................................................................... 48 Table 21: NHBO Production Trip Rates ......................................................................................... 49 Table 22: Model Trip Productions vs. Survey and 2010 Model..................................................... 51 Table 23: Employment‐based Attraction Trip Rates..................................................................... 52 Table 24: Local Model Landuses.................................................................................................... 54 Table 25: Santa Barbara Final Land Use Trip Rates....................................................................... 57 Table 26: Lompoc Final Landuse Trip Rates .................................................................................. 59 Table 27: Goleta Final Landuse Trip Rates .................................................................................... 60 Table 28: Santa Maria Final Landuse Trip Rates............................................................................ 60 Table 29: Unbalanced Trip Attractions Comparison ..................................................................... 61 Table 30: Unbalanced Trip Productions and Attractions .............................................................. 63 Table 31 Occupancy Factors.......................................................................................................... 64 Table 32: Weighted IX Survey Trips............................................................................................... 66 Table 33: In‐Commuting and Out‐Commuting Worker Estimates................................................ 69 Table 34: Final Peak Balanced Productions and Attractions......................................................... 71 Table 35: Final Offpeak Balanced Productions and Attractions.................................................... 71 Table 36: Model Friction Factor Parameters................................................................................. 74 Table 37: Destination Choice Utility Coefficients.......................................................................... 78 Table 38: Destination Choice HBW Model .................................................................................... 78 ATTACHMENT 1
5 Table 39: Destination Choice HBShop Model................................................................................ 78 Table 40: Destination Choice HBO Model ..................................................................................... 79 Table 41: Survey vs. Model Trip Lengths and Travel Times........................................................... 81 Table 42: Mode Choice List of model coefficients ........................................................................ 83 Table 43: Observed Peak Mode Shares by Trip Purpose............................................................... 93 Table 44: Model Peak Mode Shares by Trip Purpose.................................................................... 93 Table 45: Observed Offpeak Mode Shares by Trip Purpose ......................................................... 93 Table 46: Model Offpeak Mode Shares by Trip Purpose............................................................... 94 Table 47: Model Time Periods....................................................................................................... 95 Table 48: Trip Diurnal Factors by Trip Purpose ............................................................................. 95 Table 49: HOV Occupancy Rates ................................................................................................... 96 Table 50: Auto Vehicle Trips by Time Period................................................................................. 96 Table 51: Peak and Offpeak Transit Trips...................................................................................... 96 Table 52: All Day Walk and Bike Trips ........................................................................................... 97 Table 53: SCAG 2003 Truck Trip Rates .......................................................................................... 97 Table 54: SBCAG Truck Trip Rates ................................................................................................. 97 Table 55: Truck Friction Factors .................................................................................................... 98 Table 56: Truck Diurnal Factors..................................................................................................... 99 Table 57: Truck Trips by City.......................................................................................................... 99 Table 58: Truck Vehicle Trips by Time Period................................................................................ 99 Table 59: VMT and VHT by Functional Class: Base Year.............................................................. 101 Table 60: Daily Model Volumes vs. Counts ................................................................................. 102 Table 61: Daily Model Volumes vs. Counts by District................................................................ 102 Table 62: Daily Model Volumes vs. Counts by Screenline........................................................... 103 Table 63: Sensitivity Results Table 1............................................................................................ 109 Table 64: Sensitivity Results Table 2............................................................................................ 110 Table 65: Demographic Summaries for Future Years.................................................................. 112 Table 66: Trip Comparisons Between Base Year 2015 and 2050................................................ 113 Table 67: Trip Length Comparisons Between Base Year 2015 and 2050 .................................... 114 Table 68:Mode Share Comparisons Between Base Year 2015 and 2050.................................... 114 Table 69: OD Vehicle Trips Between Base Year 2015 and Future Year 2050.............................. 116 Table 70: Transit, Walk, and Bike Trips Between Base Year 2010 and Future Year 2040........... 116 Table 71: VMT and VHT Comparisons between 2015 and 2050 Scenarios ................................ 119 Table 72: Daily Transit Ridership Comparisons between 2015 and 2050................................... 119 ATTACHMENT 1
6 List of Figures
Figure 1: SBCAG Landuse‐Travel Model Interface......................................................................... 10 Figure 2 Final TAZ Structure .......................................................................................................... 13 Figure 3 Population Synthesis Household and Population Tables................................................ 29 Figure 4 1‐Person HH Auto Ownership Model .............................................................................. 34 Figure 5 Market Segment 2: Two Person Household Auto Ownership Model ............................. 36 Figure 6 Market Segment 3: Three Person HH Auto Ownership Model ....................................... 38 Figure 7 Market Segment 4: Four Person HH Auto Ownership Model ......................................... 40 Figure 8: Destination Choice Modes ............................................................................................. 76 Figure 9: Mode Choice Peak HBW Model Structure ..................................................................... 84 Figure 10: Mode Choice Peak HB Other Model Structure............................................................. 85 Figure 11: Mode Choice HB Shop Model Structure ...................................................................... 86 Figure 12: Mode Choice School Model Structure ......................................................................... 87 Figure 13: Mode Choice NHBW Model Structure ......................................................................... 88 Figure 14: Mode Choice NHBO Model Structure .......................................................................... 89 Figure 15: Mode Choice IXXI Model Structure .............................................................................. 90 Figure 16: Off‐Peak HBW Model Structure ................................................................................... 91 Figure 17:Off‐Peak HB Other Model Structure ............................................................................. 92 Figure 18: AADT vs. Model Volume Scattergram........................................................................ 101 Figure 19: Count vs. Flow Comparison by Screenline ................................................................. 103 Figure 20: Deviations in Screenline Volumes.............................................................................. 104 Figure 21: AM Flow and Congestion in South Santa Barbara...................................................... 105 Figure 22: PM Flow and Congestion in South Santa Barbara...................................................... 105 Figure 23: AM Flow and Congestion in North Santa Barbara...................................................... 106 Figure 24: PM Flow and Congestion in North Santa Barbara...................................................... 106 Figure 25: Modeled Transit Boardings vs. Ridership by Company.............................................. 107 Figure 26: Base Year 2015 (Left) vs. Future Year 2050 (Right) AM Flow Map............................ 117 Figure 27: : Base Year 2015 (Left) vs. Future Year 2050 (Right) PM Flow Map........................... 118 Figure 28: 2015 (Left) vs. 2050 (Right) AM Flow Comparisons for North Santa Barbara County118 Figure 29: 2015 (Left) vs. 2050(Right) PM Flow Comparisons for North Santa Barbara............. 118 ATTACHMENT 1
7 Executive Summary
The SBCAG model is a hybrid travel demand model that combines activity‐based population synthesis and then performs the following modeling steps: trip generation, trip distribution, mode choice, time‐of‐ day, and assignment.

For the model update, TAZs and demographics data were developed based on ACS block group 2012‐ 2016 demographics data, 2015 InfoUSA employment data, ACS Public Use Micro Sample (PUMS) data and 2015 Longitudinal Employment Dynamics (LEHD) data. The number of TAZs in the model was expanded from 1188 to 1202 zones.

Updated highway and transit networks were created for the 2015 base year model. Some centroids and connectors were changed to reflect the updated 1202 zone system. A “master network” concept was implemented for the networks which allowed the use of single datasets that include both base and future year highway and transit geography and attributes. Times, speeds, capacities and other network attributes were re‐estimated for the model update. TAZ‐to‐TAZ highway and transit network skims were estimated from the networks. For the highway networks, path minimization was based upon generalized cost with both a cost per mile and travel time component. Transit skims were based on the TransCAD Pathfinder method, which minimizes transit generalized cost and combines transit paths with similar costs.

One of the unique features of the travel model is that the first step of the model, trip generation, models individual persons and households within the county. Population synthesis is used to generate persons and households in a similar manner to the process in activity‐based models. An auto ownership model was then estimated for each individual household based upon the household size, and included 4Ds variables. The main sources for the estimation of the model structures and parameters are the ACS PUMS data, the 2012 California Household Travel Survey (CHTS), and the 2017 National Household Transportation Survey (NHTS)
Trip productions by trip purposes are then estimated for the individual persons based upon person trip rates. For trip attractions, land‐use based trip rates are derived from local city attraction models within the county. Trips from trip generation were then split into peak and offpeak period trips. Distribution and mode choice models were then estimated separately by peak and offpeak time periods.

From trip generation, the trips are aggregated by TAZ and aggregate destination choice and gravity models are applied. Destination choice models were estimated for HBW, HBShop, and HBOther trip purposes, and gravity models were estimated and applied for HBSchool, NHBW, and NHBOther trip purposes. The destination choice models included 4Ds variables as part of the parameter list.

For mode choice, nested logit choice models were estimated for each trip purpose and for peak/offpeak.

The model parameters were estimated using the CHTS and included 4Ds variables in the model parameters. The modes estimated are Auto, Shared Ride, Transit, Walk, and Bike. Mode choice target shares used for calibration were updated using the combined CHTS and NHTS survey.

ATTACHMENT 1
8 From the peak and offpeak mode choice models, the time of day models further split up the trips into 7 distinct time periods: AM, Late AM, Lunch, Early PM, PM, Evening, Late Evening, and Night. Trip diurnal factors by trip purpose were initially developed from the 2001 CHTS and updated using the blended 2012 CHTS and 2017 NHTS. Walk, Bike, and Transit trip matrices were also extracted from the mode choice models.

Drive alone and shared ride trips were then assigned by time period to the highway network. The assignment method used was N‐Conjugate User Equilibrium, and the assignments were run to a relative gap of 1e‐4. Peak and offpeak transit trips were assigned to the transit networks, and the walk and bike matrices were assigned to the walk and bike networks respectively. The rest of the document describes each of the data and model development pieces with model results in more detail.

ATTACHMENT 1
9 Caliper developed the travel model within the TransCAD 9.0 platform, utilizing the flow chart GUI interface. An example of the interface is shown below:

ATTACHMENT 1
10 Figure 1: SBCAG Landuse‐Travel Model Interface
ATTACHMENT 1
11 SBCAG Regional Travel Model Description
TAZ Development
TAZ’s Geographic Database
As part of the 2015 model update, the TAZs were updated. Several TAZ boundaries were updated to matched changed city boundaries, especially in the Santa Barbara and Goleta areas.

Some TAZs were split into smaller areas to accommodate current and future development plans.

The splits were performed to match current 2010 Census Block boundaries The updated TAZ layer has 1202 zones with 40 belonging to the external counties of San Luis Obispo and Ventura. The TAZs were updated with the following goals in mind:
 The TAZs should be aggregates of Census 2010 blocks and should overlay the Census 2010 blocks exactly. This essentially means that TAZ boundaries should not split Census 2010 blocks. This requirement is to ensure that the Census attributes can be easily aggregated to the TAZ level for both base and future year estimates.  The county of Santa Barbara includes four cities namely Santa Barbara, Goleta, Lompoc and Santa Maria. Each of these four cities has local travel demand models that rely on local TAZ boundaries. Since the local agencies have invested considerable time and effort and have used their local knowledge and expertise in developing appropriate zone boundaries for their respective models, it is desirable to retain the local TAZ structure for each of the four cities. However, the local TAZ boundaries are not constructed from census blocks and do not exactly overlay with the blocks. Therefore care has to be taken so as not to violate previous or other requirements.  For regions outside the four cities within Santa Barbara County, the county has devised an MTPS zone structure with approval from the Census Bureau. Therefore, another requirement is to design TAZ that disaggregate the MTPS blocks based on population clusters and yield reasonable sized TAZs. While splitting the MTPS zones, it needs to be ensured that each sub‐zone will have reasonable loading points for the centroid connectors. As always, it is desired that the split zones still overlay Census 2010 block boundaries exactly.
 For the external counties of San Luis Obispo and Ventura, the initial requirement is to use the Traffic Analysis Districts (TAD) for the zonal boundaries. There are 32 TADs for Ventura and eight TADs for San Luis Obispo.
 The TAZ boundaries should be contiguous and should preferably not consist of odd shapes, jagged edges, tiny slivers or holes. It would also be beneficial to ensure that TAZs are convex shaped and that the centroid for each TAZ will fall within the TAZ.

The TAZs were numbered by creating a unique identifier based upon the city that the TAZ was in and on the local TAZ ID. This identifier scheme is shown in Table 1 TAZ Numbering Scheme.

ATTACHMENT 1
12 Table 1 TAZ Numbering Scheme
Region Grouping ID Scheme
City of Santa Barbara 10000 + SB_Local_TAZ_ID
City of Goleta 20000 + Goleta_Local_TAZ_ID
City of Santa Maria 30000 + SM_Local_TAZ_ID
City of Lompoc 40000 + Lompoc_Local_TAZ_ID
MTPS zones that were not split 50000 + MTPS_SBCAG_ID
MTPS zones that were split 100000 + 100*MTPS_SBCAG_ID + Subzone_ID (last 2 digits) Ventura County 200000 + Ventura_TAD_ID
San Luis Obispo County 300000 + SLO_TAD_ID
TAZs that were split during the update were assigned an unused TAZ number that was consistent with the table above. In most cases where TAZ borders changed to reflect updated city boundaries, the TAZ IDs were left unchanged.

The table below shows the number of model TAZs by region and the figure below shows the final TAZ structure.

Table 2 Number of Model TAZs
Region Num.

Features
Buellton 14
Carpinteria 28
Goleta 74
Guadalupe 8
Lompoc 212
Santa Barbara 357
Santa Maria 86
Solvang 21
Unincorporated 362
Ventura County 32
San Luis Obispo County 8
Total 1,202
ATTACHMENT 1
13 Figure 2 Final TAZ Structure
Demographics
Socioeconomic data was obtained from the following sources. Each of these data sources were used at its highest level of available geographic detail to generate the TAZ socioeconomic inputs for the model.
 Census 2010 Summary File 1 (SF1) Census Blocks
 Census Longitudinal Employer‐Household Dynamics (LEHD) 2015 employment estimates  InfoUSA Employment Data
 SBCAG base year 2010 current model demographic data
 SBCAG Regional Growth Forecast (RGF) data
 American Communities Survey (ACS) 5 year (2012‐2016) demographic data at the block group level
 ACS 1 year, 3 year, and 5 year Public Use Micro Sample (PUMS) individual household and population data
ATTACHMENT 1
14 Census 2010 SF1 Census Blocks
This is 100% sample head count information from the Census short form. The data contains age, sex, race, Hispanic/Latino origin, and household relationship information from all people and housing units. This is the most accurate estimate of population and household information available at the highest level of detail. Model TAZs are built from Census Blocks, and most model population and housing demographic information are aggregations of the block information.

Census LEHD 2015 Job Estimates
This dataset provides census block level estimates of jobs at the workplace. The jobs are broken down by age, earnings, NAICS sector, race, ethnicity, and educational attainment. The 2015 dataset is based on Census 2010 block geography. This dataset also provides place‐of‐residence to place‐of‐work origin‐destination data. This data was considered in conjunction with the InfoUSA employment dataset to determine TAZ, parcel, and grid employment information.

The employment estimates are built upon wage records in the unemployment insurance system and information from state ES‐202 wage data. Unemployment insurance coverage is broad, covering over 90% of total wage and salary civilian jobs.

When employment numbers are compared with other employment data, exclusions to unemployment insurance coverage should be taken into account. Federal government employment is not generally included. Exempted employment varies slightly from state to state due to variations in state unemployment laws, but generally also excludes many farmers and agricultural employees, domestic workers, self‐employed non‐agricultural workers, members of the Armed Services, some state and local government employees as well as certain types of nonprofit employers and religious organizations.

The LEHD data was not a major factor in the determination of the geography of the TAZs.

Factors such as the geography of the local TAZs, geography of Census blocks, street network geography, aerial imagery, and current household and population by block were used to determine geography. After the TAZ geography was determined, the LEHD data was considered for use as employment inputs by TAZ. The LEHD data was compared with the TAZ‐aggregated InfoUSA point data and discrepancies were found. Since the InfoUSA data was considered to be more accurate and since the dataset was verified and updated by SBCAG, we decided to use InfoUSA as the main dataset for employment inputs. The LEHD data was used to estimate and verify jobs‐to‐housing flows for the external models. It was also used to verify Home‐Based Work trips in the model during the validation process.

InfoUSA Employment Data
This dataset contains a point record for every business in the county and has information on the number of employees of business and the employment type (NAICS code).

In maintaining and adding to its business database, InfoUSA references several sources including directory listings such as Yellow Pages and business white pages; annual reports; Securities and Exchange Commission (SEC) information; federal, state, and municipal government data; ATTACHMENT 1
15 business magazines; newsletters and newspapers; and information from the U.S. Postal Service.

The InfoUSA database contains information for over 20,000 businesses countywide. InfoUSA conducts annual telephone verifications with each business listed in the database and continually updates and verifies this information. InfoUSA calls every business to make sure that the most reliable information is available and add public record data from county courthouse filings. New businesses are added from sources such as new business registrations and utility hookups. InfoUSA matches and cleans the data with the USPS National Change of Address (NCOA) and Delivery Sequence File (DSF) to standardize and keep the addresses accurate. In the InfoUSA database, businesses in shopping centers are counted as separate entities (records).

Individual lessees in shopping centers are separate records, regardless of where the payroll comes from. InfoUSA measures a business based on whether sales are being generated at that location.

Database verification was accomplished by manually comparing individual records for the county top employers. InfoUSA data was also compared with the 2015 LEHD data and then matched to the 2015 base year estimate of the SBCAG Regional Growth Forecast at the city level.

SBCAG RGF Data
The demographic data obtained for the model was compared with the RGF 2015 data to ensure consistency at the household, population, and employment level.

ACS Demographic Data
The data is provided at the block group level for the 5 year (2012‐2016) dataset and at larger geographic groupings for the 3 year and 1 year datasets. In addition to the SF1 variables, this dataset includes income, vehicle ownership, age, educational attainment, mode and time of travel, and occupation information that will be used for the trip model generation. Since these data are estimates, and since they are provided at the block group rather than at the block level, this data was reconciled with the Census 2010 block level and the model TAZs. This dataset provided household marginal demographics that was not available with the SF1 block data.

Attributes that were collected from this dataset included households by vehicles, workers, and income category.

ACS PUMS Data
This dataset uses the same source data as the ACS demographics data, but contains the individual population and household responses. Certain records are suppressed and/or disguised in order to protect individual privacy. This dataset was very useful in terms of estimating the various travel models and providing basic inputs to the population synthesis or other activity based models. The ACS PUMS 2012‐2016 data was used as the primary input to the population synthesis procedure.

The following table lists the final demographic and TAZ‐based variables that were used in the travel model:

ATTACHMENT 1
16 Table 3 TAZ Variables
Variable Description
Final_ID TAZ Number
PUMA5_Int PUMA Number of TAZ
NodeID Node ID in network that TAZ is connected to
City_Flag 1=Santa Barbara,
2=Goleta
3=Santa Maria
4=Lompoc
99=Other
City Name of City
SB_Area_Type Area Type within Santa Barbara City: 1=CBD, 2=City Grid, 3=Western Suburban, 4=Eastern Suburban
County County of TAZ
Households Number of households in TAZ
IncomeCategory Average income category of TAZ (1‐8). Categories are described later HH_People <18 Number of HH with people under 18
HH_No People <18 Number of HH with no people under 18
HH_People 65+ Number of HH with people >= 65
HH_No People 65+ Number of HH with no people >= 65
HH_1_person 1 person HH
HH_2_persons 2 person HH
HH_3_persons 3 person HH
HH_4+_persons 4+ person HH
HU_Owner Households with an Owner occupant
HU_Renter Households with renter occupants
HH_IncomeCat1,2,…8 Household income by category
1: < 15K
2: 15‐25K
3: 25‐50K
4: 50‐75K
5: 75‐100K
6: 100‐150K
7: 150‐200K
8: > 200K
Single_Family_HH Single family households
Multi_Family_HH Multi‐family households
Agricultural_Employment Agricultural employment
Commercial_Employment Commercial employment
Industrial_Employment Industrial employment
Office_Employment Office employment
Service_Employment Service employment
Low_Density_Residential Low density residential dwelling units High_Density_Residential High density residential dwelling units Low_Density_Commercial Low density commercial floor space (in 1000sf units) High_Density_Commercial High density commercial floor space Office Office floor space
Industry Industrial floor space
Institutional Institutional floor space
PreSchool_Students Preschool students
Elementary_Enrollment Elementary school enrollment (public) Middle_School_Enrollment Middle school enrollment
High_School_Enrollment High school enrollment
College_Enrollment College enrollment
ATTACHMENT 1
17 Variable Description
Private_Elementary_Enrollment Private elementary school enrollment (K‐8) Private_High_Enrollment Private high school enrollment
Hotels_Motels Hotel/motel rooms
Agricultural Agricultural floor space
Parks_and_Open_Space Parks and open space acres
UCSB Number of UCSB students
Student_Housing_On_Campus Number of UCSB students living on campus Student_Housing_Off_Campus Number of UCSB students living off campus Faculty_Staff Number of UCSB faculty and staff
La_Cumbre_Mall La Cumbre Mall Floor space
Ext_Time_to_SB Time from SB County border to external zone SLO_Time_To_SB Time from SB‐SLO border to internal zone VEN_Time_To_SB Time from SB‐VEN border to internal zone KEXT Constant for external model to zone
EXT_Employment Employment in external zone
EXT_Housing_Units Households in external zone
Avg RetailEmp_Density Number of retail employment within 0.5 mile / area Avg Retail&ServiceEmp_Density Number of retail and service employment within 0.5 mile / area Avg TransitStop_Density Number of transit stops within 0.5 mile of TAZ/area Avg Employment_within_1.5miles Employment within 1.5 miles of TAZ ParkingCost Daily parking cost at TAZ
Half_ParkingCost Half parking cost
CBD_Dummy Dummy variable for CBD zone
AreaType 1=CBD, 2=Urban, 3=Suburban, 4=rural
The 2012 CHTS travel survey was primarily used for the travel model estimation, since that was the most recent and widespread travel survey available for Santa Barbara County. This survey was blended with the 2017 National Household Travel Survey (NHTS) to form a combined survey dataset.

Network Development
The highway network is represented as a GIS geographic file. Both the attributes and the geography of the network are maintained in the TransCAD GIS. The geographic file uses shaping between nodes to accurately define both the true geography and true distances of the network links. Two primary data sources were used to help define the true geography of the network links: the aerial photo layers provided by SBCAG, and the highway network from the previous network. There are approximately 32,000 directed links and 12,500 nodes in the base year 2015 network. The 2015 base case network is housed within a “master” network that contains link geography and attributes for all model horizon years. In the master network concept, only link geography that is different from the base case is added for future year links. Extra attribute fields are also defined for future year attributes that are different from the base. The number of highway line GIS segments and nodes for the 2015 and master networks are summarized below:

Table 4 Network Nodes and Links
2015 Master
Links 17,700 17,863
ATTACHMENT 1
18 Nodes 12,723 12,742
Several steps were taken to generate the base year 2015 highway network for the model update:
 The 2010 base year highway network from the older model was used as the starting point.
 To accommodate the updated TAZs (from 1,1888 to 1,202), extra centroids and connectors were added and updated in the zones that were split. Connector nodes and zones were also updated in areas where TAZ boundaries were adjusted.  New projects that occurred between 2010 and 2015 were determined and coded into the highway network.

Network Attributes
The following table lists all of the database fields in the highway network:

Table 5 Network Attributesetwork Attributes
Field Description
ID Internal Line ID automatically generated by TransCAD Length GIS Length determined by TransCAD
Dir 0 = Two way street, 1 or –1 = One way street
Number Route Number
NAME Street Name
[Functional Class] Text description of functional class AREATYPENAME Values of CBD, URBAN or RURAL to match AREATYPE field ABLANE & BALANE*^ Number of lanes in AB and BA direction AB_Speed_Limit & BA_Speed_Limit Speed limits
BIKE_CLASS Bike Class of link:
1 = 10mph
2 = 12mph
3 = 15mph
UPlan Functional class name input for UPLAN raster layers Original_Link_ID Link ID of 2000 network
County County Name of Link
Listed_Modification Flag for modification of link from 2000 network Added_Link Added link from 2000 network
Source_Network Source for network link (e.g. CA20LINK = original network, SCAG‐VENTURA = SCAG Network, etc.

CCSTYLE Field used to custom color links
LINKTYPE*^ Functional classification of links
1 = Freeway
2 = Expressway
3 = Principal Arterial
4 = Minor Arterial
5 = Urban Collector
6 = Rural Collector
8 = Local Roads
9 = Centroid Connectors
10 = Ramp
11 = System‐to‐System Ramp
12 = HOV Links (Currently not used)
ATTACHMENT 1
19 Field Description
15 = Exclusive Bike Trail
13 = Roundabout (Currently not used)
99 = AMTRAK Rail Link
999 = Connector link between AMTRAK and Highway Network [Functional Class] Text description of functional class SIGNROUTE US or State Route number, used for labeling feature displays [SIGN CLASS] Class type of SIGNROUTE (US, State, or Freeway/Expressway) AREATYPE*^ Geographic type of link: (1 = CBD, 2 = URBAN, 3 = RURAL) AREATYPENAME Values of CBD, URBAN or RURAL to match AREATYPE field AUX_LANE*^ Flag field to determine if freeway link includes an additional auxiliary lane, for lane capacity calculation purposes
GEOCONSTRAINT*^ Flag field to determine if link speed and/or capacity is constrained geographically due to steep grades, narrow lane widths, etc.

ALPHA* Alpha parameter of BPR equation (calculated by model) BETA* Beta parameter of BPR equation (calculated by model) From_PM The From Postmile value in the Caltrans postmile point database To_PM The To Postmile value in the Caltrans postmile point database MODE*^ 100 = Highway link, used to code non‐transit links in transit network CALTRANS_COUNT Obsolete
CALTRAN_PEAK_FLOW Obsolete
ABOVERSPEED1 and
BAOVERSPEED1*^
Model free‐flow speed override field, used for calibration City*^ 1 = Santa Barbara, 2 = Santa Ynez, 3 = Santa Maria, 4 = Lompoc, Missing = Outside of SB County
EXTERNAL_LINK* Flag field to denote link bordering county line AB_SPLIT and BA_SPLIT*^ Field used for LOS Calculation (see LOS Documentation) (Obsolete) CentroidConnector*^ Field used for LOS Calculation (see LOS Documentation) ABOVERCAP and BAOVERCAP*^ For new projects, denotes additional absolute capacity values to add to base capacity. Used to model projects that don’t reflect additional lanes but add to capacity (e.g. widened turning bays, etc.)
CD Census Division of link
SC Flag for South Coast Links
Section_SC TO VTA Section of 101 from South Coast to Ventura County Line Section_SC Section of 101 from South Coast to Milpas
Heading Azimuth of link
Control_station SBCAG control station code used for count links Station_Location Longform name of control station
SCREENLINE* Screenline name for count link
[AB 1] & [BA 1], [AB 2] & [BA 2], etc.
**
Count in both directions for the first hour, second hour, etc. up until the 24th hour. In model for calibration, the 8th, 13th and 17th hours are used to model the AM, Midday and PM hours respectively
AB_AADT and BA_AADT* and
Total_AADT
Daily link count in both AB and BA directions and Total AADT in both directions AB/BA_CMP_PM_Count PM peak hour count from Congestion Management Plan ABFREESPEED & BAFREESPEED* Free‐flow speed in AB and BA direction (calculated by model) Count_Year Year that count was taken (typically 2007‐2010) ABFREETIME & BAFREETIME* Free‐flow travel time in AB and BA direction (calculated by model) CMP_Count_Year Year that CMP count was taken
Count_LinkID Link ID of count in GROUND COUNT 2000‐based GIS database Count_Flag 1 = Count exists on link
New_Count Recently inserted count
F1_Count F1 Code of count from Dowling
CID_Count CID Code of count from Dowling
CountSource Source of Count: Ground Count, Caltrans, Dowling, CMP, etc.

RTP_ID_<1,2,3> RTP 2040 ID of project on link 3 fields are used in order to denote links that may be part of more than 1 RTP project
ATTACHMENT 1
20 Field Description
MasterNetworkEdit Flag for edit of link
Project_List_<1,2,3> Type of project, either programmed, planned, illustrative, etc. (Field is only for informational purposes)
Base_Link_Only Flag field to indicate that link exists only in the base year Future_Link_Only Flag field to indicate that link exists on in the future year ProjectYear<1,2,3> Year of project (Field is only for informational purposes) LINKTYPE_RTP_ID_<1,2,3> Link classification for the project AB/BALANE_<period>_RTP_ID_<1,2,3> Number of lanes by period for the project AUX_LANE_RTP_ID_<1,2,3> Auxilary lanes for project
BIKE_CLASS_RTP_ID_<1,2,3> Bike classification for project AB/BA_OVERCAP_RTP_ID_<1,2,3> Added capacity for project AB/BA_OVERSPEED_RTP_ID_<1,2,3> Override speed for project Field names denoted with a “*”, e.g., Total AADT*, are required fields for the model. Field names denoted with a “**”, e.g., AB1 & BA1 **, are required only for the base case network that includes counts. Of the fields that are required for the model, some are input fields and some are output fields calculated by the model. Input fields that are necessary to code in whenever links are added or modified are denoted with a “^”.

The highway network also contains traffic counts compiled by SBCAG, Caltrans, and the local agencies. Both hourly and daily Annual Average Daily Traffic (AADT) counts were compiled. Most counts are for year 2015, but some counts stretch back to 2012. Only weekday counts were included. In cases where more than one count existed on the same line segment, an average count was calculated.

Node Attributes
In TransCAD, nodes are an additional layer in the highway geographic database. Nodes have unique ID’s and optional attribute fields. In the model, a coded field denotes nodes that are centroids and the TAZ ID of the centroid node. That field is called “TAZ”, and non‐centroids have a missing value in that field. The following chart describes all of the fields in the node layer of the network:

Table 6 Node Network Attributes
Field Description
ID Internal Node ID automatically generated by TransCAD, nodes 1‐405 reflect the TAZ number Longitude, Latitude Longitude and Latitude position of node automatically determined by TransCAD CCSTYLE 0 = Two way street, 1 or –1 = One way street
NAME Street Name
CCSTYLE Node styling used to color and label nodes. Choose Map‐Feature Display for a list of CCSTYLE code numbers and its respective node style and label setting [CMP Intersection] CMP Intersection number used for LOS analysis. Currently coded as 1 to 25 to reflect the initial 25 intersections
[CMP Intersection Name] Text description of CMP Intersection TAZ TAZ number for centroid nodes, missing value for non‐centroid nodes Postmile Postmile number transferred from Caltrans postmile Shapefile database Route Route number associated with the postmile
PARKINGLOT Flag field used to determine if a node is a drive‐to‐transit parking lot.

ATTACHMENT 1
21 ATTACHMENT 1
22 Master Network Extraction and Highway Network Creation From the master line geographic file, the user modifies an RTP Project Database input to specify which highway, transit, and bike projects to include in the scenario, and the model will extract links consistent with that database input. The model also creates extra fields that will be used to calculate travel speeds, times, capacities, and flows. A TransCAD network is created from these speeds, capacities, and times. Centroids have also been set in this network. The model includes a button that automatically recreates the highway network. The model also has a supplementary input file used to estimate free flow speeds, free flow travel times, capacities, and transit travel times: a speed‐capacity lookup table.

Speed‐Capacity Lookup Table (SpdCapLookup.bin)
The speed‐capacity lookup table is used to define link free flow speeds, peak period capacities, alpha and beta parameters, and transit times for all links. These values are determined by the link functional classification (LINKTYPE), and other network attributes. By matching up the LINKTYPE, AUX_LANE, AREATYPE, and GEOCONSTRAINT fields in the network with this lookup table, the model automatically fills in the following fields: ABFREESPEED, BAFREESPEED, ABPEAKCAPACITY, BAPEAKCAPACITY, ABDAYCAPACITY, BADAYCAPACITY, TRANSIT_MULTIPLIER, ABFREETIME, BAFREETIME, ALPHA, BETA, ABTRANSITTIME and BATRANSITTIME. The model also uses the fields AB_LANES and BA_LANES as inputs network fields.

In general, freeway capacities are approximately 2,100 vehicles/lane/hour, which are fairly standard values recommended for modeling purposes. The capacity estimates came largely from the 2000 Highway Capacity Manual and recommendations from Dowling Associates. Alpha and Beta values vary by link classification and area type. Bike speed lookups are used to determine bike speeds depending on link classification. Bikes are typically prohibited on freeways, expressways, and ramps.

Calculation of Transit Times
The lookup table is also used to calculate estimates of transit travel times on the network links.

The R_TRMULT field in this table fulfills this role. This value is multiplied by the congested travel time to calculate the transit travel time. In general, freeways and major principal arterials have less bus stops than collectors and local roads, and thus less of a difference between the highway and transit congested times. Thus, freeway and arterial links will be assigned a lower multiplier value than collector and local links.

Link Adjustments
After the lookup tables are applied to the network, speeds and capacity can be adjusted on an individual link level. The speeds are adjusted using the ABOVERSPEED1 and BAOVERSPEED1 fields, while the capacities are adjusted using the ABOVERCAP and BAOVERCAP fields. The OVERSPEED fields replace the lookup speed with the specific link‐calibrated speed while the OVERCAP fields add onto the capacities specified in the lookup table.

ATTACHMENT 1
23 Transit Geographic Database
The highway network is represented as a TransCAD Route System. A route system is a special GIS data structure that allows the coding of transit routes, stops and all associated attribute information. The route system uses as its base geography, a line geographic database. Thus, both the highway database and the transit routes are linked together. This coupling provides several advantages from both a modeling and data maintenance perspective:
 Changes in the highway network are immediately reflected in the transit routes  Any of the attributes used for the highway network are used for the transit network (e.g. the congested travel times)
 The same set of centroid connectors are used for both the highway and transit network For the model, the transit routes were updated from the old 2010 base year. 2015 GTFS feeds from SBMTD, SMAT, and VCTC were imported and integrated into the existing routes. Schedules from the GTFS feeds were used to update route frequencies. Fares for all routes and headways for other routes were updated from agency web site information. There are 355 directional routes and 5,320 stops in the master transit route system.

Transit Network Attributes
Transit network attributes come from three possible data sources in the route system: the base line geography which is the source of transit link‐level attributes, the route layer which is the source of attributes across the entire route (e.g headways), and the stop layer which provides attributes for single stops.

Since the base line geography for the transit route system is the highway network, the transit network also inherits all of the attributes. The attributes for the highway network were described in the previous section. The transit network only uses the LENGTH, ABTRANSITTIME, BATRANSITTIME, and MODE fields from the highway database.

The route layer contains several attributes. These are described below:

Table 7 Route Layer Attributes
Field Description
Route_ID Internal TransCAD‐assigned ID
ROUTE_NAME Unique name for the route, also required by TransCAD, e.g., Eastside S. and Eastside W.

BIGROUTE Route Family, e.g., Eastside
SIDE Displays route on either “R” right or “L” left side of the link LINE NUMBER Line number of route
Direction Direction of route (N,S, E,W)
AM AM Headway
PM PM Headway
MIDDAY Midday Headway
[ALL‐DAY] All‐day headway (used in model)
OPERATING_TIME_AM Time of operation in the AM period
OPERATING_TIME_PM Time of operation in the PM period
OPERATING_TIME_MIDDA
Y
Time of operation in the midday period
OPERATING_TIME_ALL‐ Time of operation for the full day
ATTACHMENT 1
24 Field Description
DAY
MODE_NUMBER 1=Trolley
2=Local Bus
3=Express Bus
MODE Long explanation of mode_number
Company Amtrak
Breeze
COLT

CUYAMA TRANSIT
Central Coast Shuttle
Chumash Casino Resort
City of Guadalupe
Clean Air Express
Coastal Express Limited
Greyhound
MTD = Santa Barbara MTD
SLO RTA = San Luis Obispo RTA
SMAT = Santa Maria Area Transit
SMOOTH
SYVT = Santa Ynez Valley Transit
Santa Barbara Airbus
VISTA

WINE COUNTRY EXPRESS
COMPANY_NUMBER Company number of Route
FARE 1‐way average fare
LOCAL_WEIGHT Route weighing factor applied to the local skims and assignments EXPRESS_WEIGHT Route weighting factor applied to the express skims and assignments LENGTH Route length
[DAILY RIDERSHIP] Year 2015 route daily ridership count estimate [ANNUAL RIDERSHIP] Year 2015 route annual ridership count estimate [AGGREGATE RIDERSHIP] Ridership count within the route family.

RTP_ID RTP 2045 ID of project
AM/PM/MIDDAY_ALLDAY
_RTP_ID
Period headway of project
MODE_NUMBER_RTP_ID Mode number of project
COMPANY_NUMBER_RTP
_ID
Company number of project
COMPANY_RTP_ID Company name of project
FARE_RTP_ID Fare charged for route project
Future_Route_Only Flag field to denote route that exists only in the future year Base_Route_Only Flag field to denote route that exists only in the base year Route Stop Attributes
In TransCAD, a route stop is defined as a stop that is specific for a particular route. Thus it is both possible and likely to have multiple route stops at the same location if several routes have “stops” there. Below is a list of the attributes for the route system route stop layer:

Table 8 Route Stop Layer Attributes
Field Description
ID Internal TransCAD‐assigned ID
Longitude Longitude of stop
Latitude Latitude of stop
Route_ID Route that stop is associated with, linked to the Route_ID field in the route layer Pass Count TransCAD internal field, used to determine stop location if route passes by the link multiple times Milepost Milepost location of route, assuming that the milepost value at the beginning of the route is zero STOP_ID Internal TransCAD‐assigned ID
Field_1, Field_2, Field_3,
Field_4
Not used
ATTACHMENT 1
25 Field Description
nodeid Highway node ID that the stop is associated with. Used for “snapping” during network creation AMTRAK_CITY For Amtrak stops, the station name
AMTRAK_TIME Time to the next Amtrak station (not used in model) Transit Network Creation
From route system and underlying line geography, a TransCAD transit network is created. A transit network consists of two types of links; transit links and non‐transit links. A transit link is created between each route stop of each route in the transit network. Transit links may also be referred to as route segment links. The attributes for each transit link are transferred from the underlying line geography. A non‐transit link is defined as a selection set of the underlying line geography, and represents access/egress and transfer links.

Transit Network Attributes
The network uses the LENGTH, ABTRANSITTIME, BATRANSITTIME, WALKTIME and MODE fields from the highway network. All of these fields were described in the highway networks section.

For the transit links, the fields ABTRANSITTIME and BATRANSITTIME are used as estimates of route segment travel times. WALKTIME is the walk time for each link.

Non‐Transit LInks
For transit network creation, all of the routes except for Amtrak, are used for the transit network. For the non‐transit links, all of the highway network links except for the bike trailways are used. The definition of access/egress and transfer links is different than the classical approach. In the classical approach, there is a separate single link connecting each origin to each stop location that is accessible to the origin. For park‐and‐ride stops that can be accessed by many origin TAZ’s, this can generate many links. If this network were drawn geographically, it would resemble a typical “spider” network with many links emanating out of several nodes. In the SBCAG model, the entire underlying highway network is used as the access/egress and transfer links. Thus, instead of one link being defined as the connection between TAZ and stop, the entire highway network can be used to go from TAZ to stop. This paradigm has several advantages over the classical one:
 Estimated travel times tend to be more accurate as they come straight from the network links travel times themselves instead of being estimated again.  Visually, this network is more understandable than the one that contains multitudes of “spider” links.
 You do not need to manually define each TAZ‐to‐stop pairing.

The maximum TAZ‐to‐stop time is limited by a maximum access time and egress time parameter in the transit network settings.

Stop Snapping
Route stops in TransCAD are merged together in the transit network to provide connectivity between the routes to enable route transfers. For each route stop, you define the highway network node ID it is associated with. During network creation, TransCAD will merge route stops ATTACHMENT 1
26 with common node ID values into a single node in the transit network. For the SBCAG model, this is an automatic operation. The model determines, for each route stop, the nearest associated node ID and then will “merge” based on these node ID’s. The node ID value that is determined for each stop is stored in the “nodeid” field in the route stops layer.

Transit Network Settings
From the route system, a generic peak and offpeak transit network is created. In this generic network, both walk and drive access to stops are allows. For the walk access, trips go from the centroid to near stops using the WALKTIME field as their impedance. All stops are available for access in the walk‐to‐local network. For the drive access, only a selection of stops is available for access. These stops are park‐and‐ride nodes in the node layer. The stop locations are designated in the highway node layer attribute called PARKINGLOT, with a parking node being coded a value of 1. In these cases, trips from the centroid to the available stop use the AM congested time fields as their impedance for the peak network, and the LUNCH congested time fields for the offpeak network.

ATTACHMENT 1
27 Highway and Transit Skims
Highway Skimming
Once the highway network is created, the model performs highway skims from centroid node to centroid node. Initially, free‐flow travel times are used. In later iterations, MSA averaged congested travel times are used. Congested travel times are calculated during the highway assignment stage, which will be described later. Both peak and offpeak skim matrices are created. Peak skim matrices are based upon the AM (7‐9am) period. Offpeak skim matrices are based upon the LUNCH (12pm‐2pm) period.

For the skimming procedure, the minimization function is based upon generalized cost, with both a time and a distance‐based operating cost component:

GC = Time + Distance*[Auto Operating Cost] / [Value of Time] For the base year 2015 model, auto operating cost is set to 19.3 cents/mile and value of time is set to $7.05 / hr. These are the parameters used in the SCAG model. The addition of generalized costs allows the model to be sensitive to changes in gas prices in the form of the auto operating costs. As gas prices increase, auto operating costs will increase, making the model more sensitive to the distance component of the generalized cost function.

After travel time matrices are calculated, intrazonal travel times are calculated and then origin and terminal times are added to them. The procedure used for calculating intrazonal travel times was the closest neighbors approach. For each row origin, the travel time of its two closest neighbors is found from the matrix. The intrazonal time is then just the average of the two closest neighbors. The maximum limit of intrazonal times was set to 15 minutes. This was done to mute the effect of large estimated travel times in large area zones.

After intrazonal travel times are estimated, additional origin and terminal times are added to the skim matrix. The additional travel times were set to an extra 2 minutes for both origin and terminal time for all zones, resulting in an additional total of 4 minutes of additional time.

The highway skims proceeds in the following manner:

1. The highway database and network are opened and nodes that are centroids are selected.

2. A travel time matrix from centroid to centroid is calculated assuming free flow travel time for the first iteration skim, and the assignment‐based congested time for the feedback skims.

3. Intrazonal times taking into account nearest adjacent neighbor times are calculated and capped to a maximum of 15 minutes.

4. Origin and terminal times are added to the matrix.

ATTACHMENT 1
28 Transit Skimming
Once the transit network is created, the transit skims are computed from centroid node to centroid node. TransCAD minimizes “generalized costs” when determining the best path between origin to destination nodes. The generalized cost is a mix of in‐vehicle travel times, access, egress and transfer times, waiting times and fares. The model uses the “Pathfinder” method for both skimming and assignment, which combines routes together into trunk links when determining best paths, thus reducing actual waiting times. During assignment, riders on trunk links would be distributed based on the relative frequency of the routes within the trunk.

Two skim matrices are calculated: one for the peak period and one for the offpeak period. The following fields are used when determining the best transit paths:

Table 9 Transit Best Path Fields
Field Source Used for

ABTRANSITTIME &

BATRANSITTIME
Highway line database Transit in‐vehicle times for all skim matrices WALKTIME Highway line database Access, egress and transfer links for walk‐local and walk‐express networks, transfer and egress
links for drive‐local and drive‐transit networks
ABCONGTIME_AM/OP &
BACONGTIME_AM/OP
Highway line database Access links for drive‐transit networks [ALL‐DAY] Route layer field Determination of waiting times (1/2 of headway with a maximum wait of 15 minutes)
FARE Route layer field Cost calculation, with conversion rate of 20 cents/minute or $12.00/hour
The following tables are estimated for each skim matrix:

Table 10 Transit Skim Tables
Table Description
GENERALIZED COST The total weighted cost from origin to destination FARE Total transit fare from origin to destination
IN‐VEHICLE TIME In vehicle transit travel time
INITIAL WAIT TIME First transit combined waiting time
TRANSFER WAIT TIME Total transfer waiting time on subsequent routes TRANSFER WALK TIME Walk times on transfers between routes ACCESS TIME Access times
EGRESS TIME Egress times
DWELLING TIME Total time spent dwelling on bus
NO OF TRANSFERS Number of Transfers
ATTACHMENT 1
29 Trip Generation
Population Synthesis
The updated SBCAG model takes an activity‐based approach to trip generation. In the first step of trip generation, a procedure called Population Synthesis is run. The Population Synthesis procedure uses base Census block, block group, TAZ, and Public Use Microsample (PUMS) data to produce individual household and person records. The PUMS dataset contains individual household and population samples with their socioeconomic traits. The procedure “picks” records from this database in a methodology that matches block and block group person and household socioeconomic marginals. The result is a data record for each household and person in Santa Barbara County. Each household and population record contains socioeconomic traits relevant to the model, and the summaries of these socioeconomic traits match Census block and block group summaries. The following datasets were used in the population synthesis procedure:
 ACS 2012‐2016 PUMS data
 2010 Census Block Layer marginals
 Block Group Layer with ACS Data marginals
The socioeconomic variables that were extracted from the PUMS dataset are as follows:
 Households by Size
 Households by Tenure
 Households by presence of persons aged under 18
 Households by presence of persons aged 65+
 Households by Income Category,
 Households by Number of Vehicles
 Persons by Age
 Persons by Gender
 Persons by Employment Category
Below is an example of the output household and population tables:

Figure 3 Population Synthesis Household and Population Tables ATTACHMENT 1
30 The total results of the household and population synthesis outputs are shown below:

Table 11 Population Synthesis Household Summary
Households Total
HH_Size1 46,301
HH_Size2 59,303
HH_Size3 29,170
HH_Size4 27,528
HH_Size5Plus 13,750
HH_Veh0 12,259
HH_Veh1 54,977
HH_Veh2 67,353
HH_Veh3Plus 41,463
HH_IncLT25 35,776
HH_Inc25to50 40,153
HH_Inc50to75 29,577
HH_Inc75to100 21,491
HH_Inc100to150 25,833
HH_Inc150to200 11,357
HH_IncGT200 11,865
Table 12 Population Synthesis Person Summary
Persons Total
Male 215,480
Female 224,926
Age_LT5 22,950
Age_5to14 49,848
Age_15to19 31,337
Age_20to24 41,740
Age_25to44 107,564
Age_45to64 114,519
Age_65Plus 72,448
Agriculture 16,570
Manufacturing 22,167
Const_Util_Transp 22,820
Wholesale 5,582
Retail 28,542
FIRE 49,142
Education 29,025
ATTACHMENT 1
31 Persons Total
HealthCare 24,435
Service 57,795
PublicAdmin 14,082
Unemployed 170,246
Auto Ownership Model
The auto ownership models for the SBCAG travel demand modeling update are key models to demonstrate the connection between transportation and land use. The objective is to develop auto ownership models that are sensitive to demographics and also to 4D smart growth variables that reflect smart growth patterns. The hypothesis is that smart growth mixed‐use communities influence the number of autos owned by households in these communities, which then has a direct ripple effect in terms of the number of Auto trips and consequently the amount of VMT generated by households.

Typically, these auto ownership models are meaningful at the local level. Estimating the models using large zones and applying the models to large zones undercuts the effect of local smart growth incentives. This is because variables that capture local land use changes at the household and neighborhood level lose their significance when aggregated to a larger scale such as a model TAZ. Further, the demographics of individual households and their composition affect the response to land use incentives and these effects are also lost at the aggregated TAZ level. For instance, high income households may not reduce auto ownership as much as low‐ income households due to the development of mixed land use in their neighborhood.

For the base model year 2015, there is no need of an auto ownership model since the synthesized population database already contains the number of autos owned by each household and this information matches the ACS households by autos shares. However, for the future year model runs and for evaluating future year land use strategies, the auto ownership model becomes a key component. It is likely that marginals of households by auto ownership will not be available for the future years and even if so will not account for the 4D effects.

Therefore, the models were estimated at the household level to better capture the effects of the smart growth variables. For the SBCAG model, the Multinomial Logit (MNL) auto ownership model was estimated using the synthesized population for the 2015 base year. The 4D variables generated at the block level augment the estimation dataset. Estimating the auto ownership model using such a dataset therefore has several key benefits, the two primary being the large sample size available for estimation and the ability to include smart growth variables at the disaggregate block level. Once these models have been developed, they are used to predict the auto ownership given future land use scenarios. The classification of autos per household are then fed into the downstream trip generation models.

ATTACHMENT 1
32 Population Synthesis Input
A synthetic household and population database was developed using the PUMS 5 year ACS 2012‐2016 survey as the seed. Marginal household and population demographics were derived from the block group ACS and Census 2010 block data. At the block group level, the synthesis algorithm matched households by income and households by vehicles. The algorithm also matched households by size, households by tenure, households by presence of people under 18 and households by presence of 65+ people at the block level. The resulting synthesized database has one record for each household in the SB County region, with data on the household size, the number of vehicles owned by the household, the income category of the household, the tenure of the household (owner or renter), whether the household has members under the age of 18 and whether the household has members over the age of 65. In addition to having rich attributes at the household level, each household is also sufficiently rich in geography since each household has an attribute indicating the census block to which the household belongs.

Household demographics, when aggregated, accurately match the marginals at the Census block and block group level.

The variables from the synthesis and (other dummy variable created) relevant to the auto ownership model are shown in the table below:

Table 13 Auto Ownership Variables
Variable Description
VehChoice Choice variable for the estimation
1 ‐ Zero vehicles in HH
2 ‐ One vehicle in HH
3 ‐ Two Vehicles in HH
4 ‐ Three plus vehicles in HH
HH_SizeSegment The variable used for the market segments 1 ‐ One person HH
2 ‐ Two person HH
3 ‐ Three person HH
4 ‐ Four plus person HH
Tenure_Dummy 1 if HH is owned (as opposed to rented)
LowIncDummy 1 if HH income is below 50K, 0 otherwise
HighIncDummy 1 if HH income is above 100K, 0 otherwise
R18 1 if HH has one or more members 18 or under, 0 otherwise Age65plus 1 if HH has one or more members over the age of 65, 0 otherwise Generation of 4D variables
The 4D variables that were chosen in the final auto ownership models are shown below: These variables were only generated for blocks in Santa Barbara County.

ATTACHMENT 1
33 Table 14 4Ds Variables
Model Estimation Results
The model to estimate auto ownership is an MNL model with the choices being 0 vehicles, 1 vehicle, 2 vehicles and 3+ vehicles. Several attempts were made at model estimation and only the final results are shown here. In particular, all of the 4D variables were tested and several configurations (generic vs alternate specific) were experimented. For 2015, this model is unchanged from the base year 2010 model.

The main finding was that the estimation of 4 separate models based on the household size (1,, 2, 3 and 4+) yielded the lowest combined log‐likelihood and hence the best model. This is also in tune with our reasoning that households with different characteristics make different choices in response to other demographics and in response to smart growth incentives.

The models for the 4 segments along with the results and a brief discussion are shown next:

Market Segment 1: One Person Household
The figure below shows the model for a 1‐person HH including the estimated values of the coefficient. The Alternate Specific Constants (ASCs) are shown in the subsequent figure.

Variable Description
Jobs_HH_Diversity 1‐ [ABS(b*HH ‐ EMP)/(b*HH + EMP)],
where b = regional employment / regional households.

HH and Emp are the households and employment within a half mile buffer from the block centroid.
empwalk The number of employment opportunities within 1.5 mile walking distance (straight line) from the block centroid.

TransitStop_Density The density of transit stops within a half mile from the block centroid.

ATTACHMENT 1
34 Figure 4 1‐Person HH Auto Ownership Model
ATTACHMENT 1
35 Some observations of the model are:
 Owned households (as opposed to rented) are likely to have more autos than less.  A low‐income single person HH will most likely be inclined to having zero or one vehicle.

This justified by the positive coefficients of the LowInc dummy variables. Further, it is more likely that these households will have no vehicle (as demonstrated by the higher LowIncDummy co‐efficient for this alternative).
 Likewise, the positive value of the HighIncDummy indicates that a high‐income individual is more likely to have 3+ vehicles.
 If the only person in the HH is 65+, it is likely that he/she will not have a car as shown by the positive co‐efficient of the Age65plus variable. Note that a positive coefficient means a larger utility and hence a larger probability of choosing this alternative.  The presence of mixed land use (i.e. the Jobs_HH_Diversity variable) reduces auto ownership as shown by the positive coefficients of the 0Veh, 1 Veh and 2Veh alternatives. Further, the 0Veh alternative has the largest co‐efficient for this variable, making it more likely.
 Likewise, the presence of more walkable employment (empwalk) within 1.5 miles of the zone and the higher density of transit stops tend to promote the use of fewer vehicles.  The values of the ASC's (The alternative 1Veh is the base) are also intuitive. The value of the ASC (0) is highest for the 1Veh alternative, followed by 2vehicles, 3+vehicles and 0 vehicles alternatives.

ATTACHMENT 1
36  All the variables are very significant as seen by the t‐stats.

Figure 5 Market Segment 2: Two Person Household Auto Ownership Model The model for 2 person households is shown above. The model differs from the previous segment in:
 The Jobs_HH_Diversity is now a generic variable and only affects whether the HH has more than one vehicle or not.
 The variables empwalk and transit stop density have an equal effect on 2 and 3+ vehicle alternatives when compared to 0 and 1 Veh alternative.
 The variable R18 is added for the 2 and 3+ vehicle alternatives. Given that the market segment being considered is 2‐person HH, it is more likely that two autos may not be ATTACHMENT 1
37 required if one of them is a person under the age of 18. Thus, the coefficients of this variable for the 2 and 3+ vehicle alternatives are negative.  Likewise, if one or more of the persons are over 65, the chances of having 2 or 3+ autos reduces.
 The coefficients of the 4D variables for this market segment when compared to the previous market segment are generally and statistically lower. This makes sense since 2‐ person households in general will have the necessity to possess at least one car.  Finally, as seen in the next figure, the ASC value of all of the alternatives is lower than the base (2Veh choice) and the descending order of ASCs is 2Veh, 1Veh, 3+Veh and 0 Veh, which is intuitive.

Market Segment 3: Three Person Household
The model for the 3‐person HH segment is identical to the model for the 2‐person HH except that the co‐efficient for the LowIncDummy variable is generic for 0veh and 1Veh alternative.

Also the ASC indicates that given all else equal, these households are more likely to choose 2 autos, then 3+ autos, 1 auto and finally no auto.

ATTACHMENT 1
38 Figure 6 Market Segment 3: Three Person HH Auto Ownership Model ATTACHMENT 1
39 Market Segment 4: Four+ Person Household
The model is similar to the 3‐person HH but with different output coefficients. These households are more likely to have 2 or 3+ vehicles and the effect on auto ownership due to the presence of kids or people over 65 is less pronounced.

The ASC values are also as expected for this segment.

ATTACHMENT 1
40 Figure 7 Market Segment 4: Four Person HH Auto Ownership Model ATTACHMENT 1
41 For base year 2015 and future year scenarios, households are predicted by size, income, and tenure at the zonal or block group level. The auto availability model is run by first re‐running the synthesis for the future year using future household marginal, and then applying the auto ownership models. Note that variables like R18 and Age65plus are not expected to vary in proportion significantly for future years and thus these can be generated by the synthesis using the same proportions as that used for the base year.

For an additional check to the model, a Monte Carlo simulation of the chosen variable based on the output probabilities agreed with the estimation database. Households by size and auto distributions were also compared between the PUMS ACS dataset and the results of the auto ownership model after calibration:

ATTACHMENT 1
42 Table 15 Auto Ownership Model vs. ACS PUMS Comparison

ACS PUMS
Household
Size vs. Autos
0 1 2 3+
1 3.4% 16.7% 3.8% 0.9%
2 1.0% 7.5% 16.6% 5.8%
3 0.8% 2.9% 5.6% 5.6%
4+ 1.1% 5.1% 11.2% 12.1%
Model
Household
Size vs. Autos
0 1 2 3+
1 3.7% 16.7% 4.9% 1.0%
2 1.6% 7.2% 17.8% 7.1%
3 0.7% 3.7% 6.0% 6.2%
4+ 1.1% 3.6% 9.4% 9.3%
Trip Production Models
The trip production model estimates trip rates based upon each individual persons’ demographic characteristics (e.g. age, income, work status) and the demographic characteristic of the household that the person lives in (e.g. household income, household size, etc.). The inputs to the trip production model are the individual person and household records produced by the population synthesis and auto availability models. For each person trip record, an appropriate trip rate is determined based on the person’s and household’s characteristics. The trip rate is then applied to the person to determine the number of daily trips the person produces. Since the person’s household TAZ location is known, all of the person trips are then summarized into daily trip productions by trip purpose and TAZ.

Separate trip production models were estimated for each of the trip purposes (HBW, HBO, HBSchool, HBShop, NHBWork, and NHBOther). The CHTS 2001 travel survey was used to estimate the population trip rates. For each trip purpose, different sets of person and household demographic variables were tested for relevance in the models. The final model estimation results are described below:

Home‐Based Work
The demographic variables used for the HBW model are:

ATTACHMENT 1
43  Number of Workers in the Household
 Household income
 Autos per Worker
The general rationales for these variables are:
 The more workers in the household, the more work trips per person  The higher the household income, the more work trips
 The more autos that are available in the household, the more work trips Trip rates are derived by separating the trip survey records into the categories above and then calculating the average number of daily trips for that category. The estimated person work trip rates and survey observations by person and household category are shown below:

Table 16: HBW Production Trip Rates
Num of Workers in
HH
Household Income Autos Per
Worker
Person
Trip Rate
Observations
1 [0, 25000] All 1.230 190
1 [25000, 50000] All 1.422 496
1 [50000, 75000] All 1.452 370
1 [75000, 100k] All 1.470 137
1 [100k, 150k] All 1.003 128
1 [150k, inf] All 1.335 88
2 [0, 25000] < 1 1.335 65
2 [0, 25000] >= 1 1.352 104
2 [25000, 50000] < 1 0.392 46
2 [25000, 50000] >= 1 1.733 363
2 [50000, 75000] All 1.532 587
2 [75000, 100k] All 1.262 560
2 [100k, 150k] All 1.066 371
2 [150k, inf] All 1.287 99
3+ [0, 50000] All 1.085 265
3+ [50000, 75000] < 1 1.700 45
3+ [50000, 75000] >= 1 1.055 41
3+ [75000, 100k] All 1.196 166
3+ [100k, 150k] All 1.591 42
3+ [150k, inf] All 1.207 76
Note: HBW Rates only apply to full time and part time workers

As expected, in general trip rates increase when workers, income, and autos increase. There are a couple of anomalies in the estimation, however. In these cases, the trip rate decreases when the household income increases from $75,000‐100,000 to $100,000‐150,000 and then increases after $150,000. This may be due to workers’ increased flexibility to telecommute or reduce their work trips at this income range. All these estimations were statistically significant however, as the number of survey observations for each category is sufficiently high.

ATTACHMENT 1
44 Home‐Based Shopping
The demographic variables used for the HBShop model are:
 Whether the person is a worker or not
 Household Size
 Household Income
 Number of autos in the household
The general rationales for these variables are:
 Worker shopping rates are different than non‐worker rates and are dependent upon the number of people in the household
 The higher the household size, the more total shopping trips, but the less shopping trips per person
 The higher the household income, the more shopping trips  The more autos that are available in the household, the more shopping trips The estimated person shop trip rates and survey observations are shown below:

Table 17: HBShop Production Trip Rates
Worker or
Not
Household
Size
Household
Income
Household
Autos
Person
Trip Rate
Observations
0 1 [0, 50000] 0 0.118 36
0 1 [0, 50000] 1 0.622 220
0 1 [0, 50000] 2+ 0.898 41
0 1 >= 50K All 1.184 55
0 2 [0, 50000] 0 or 1 0.265 160
0 2 [0, 50000] 2+ 0.763 323
0 2 [50000, 100k] 0 or 1 0.249 65
0 2 [50000, 100k] 2+ 0.518 345
0 2 [100k, 150k] All 0.974 37
0 2 [150k, inf] All 0.593 74
0 3+ [0, 50000] 0 or 1 0.238 170
0 3+ [0, 50000] 2+ 0.430 557
0 3+ [50000, 100k] 0 or 1 0.243 61
0 3+ [50000, 100k] 2+ 0.127 899
0 3+ [100k, 150k] All 0.308 303
0 3+ [150k, inf] All 0.240 222
1 1 [0, 50000] 0 or 1 0.347 336
1 1 [0, 50000] 2+ 0.164 64
ATTACHMENT 1
45 Worker or
Not
Household
Size
Household
Income
Household
Autos
Person
Trip Rate
Observations
1 1 [50000, 100k] 0 or 1 0.309 128
1 1 [50000, 100k] 2+ 0.187 80
1 1 >= 100k All 0.718 38
1 2 [0, 50000] 0 or 1 0.060 80
1 2 [0, 50000] 2+ 0.495 339
1 2 [50000, 100k] 0 or 1 0.060 63
1 2 [50000, 100k] 2+ 0.393 661
1 2 [100k, 150k] All 0.775 204
1 2 [150k, inf] All 0.260 61
1 3+ [0, 50000] 0 or 1 0.165 122
1 3+ [0, 50000] 2+ 0.221 380
1 3+ [50000, 100k] 0 or 1 0.114 33
1 3+ [50000, 100k] 2+ 0.303 929
1 3+ [100k, 150k] All 0.244 299
1 3+ [150k, inf] All 0.138 202
There are a few observations in this estimation:
 Workers make more shopping trips in 1 person households when they are the only available shopper, but make less trips in two or more person households when there are other people available to make the shopping trip.
 Non‐workers make the most shopping trips in 2 person households and make less trips in 1 and 3+ person households
 Non‐workers make more shopping trips in 0 or 1 auto households vs. households that have more than 1 available vehicle.
 Workers make more shopping trips in multi‐vehicle households.  In general, more shopping trips are made as household income increases.

In some cases, the trip rate decreases when the household income increases. This could be caused by people at the top income levels reducing their shopping trips by shopping online or mail order, or by having other people shop for them. Again, all of these estimations were statistically significant.

Home‐Based School
The only demographic variable used for the Home‐based School model is the person’s age:

Table 18: HBSchool Production Trip Rates
Person Age Person Trip Rate
< 3 0
[3‐14] 1.125
ATTACHMENT 1
46 [14‐17] 0.902
[18‐24] 0.729
24+ 0.031
In this relatively simple model, the age categories reflect the typical ages for elementary, middle, high school, and post‐secondary school students.

The model takes advantage of the breakdown of school trip rates by age category to further subdivide the school trip purpose into Elementary, Middle, High School, College, Private Elementary/Middle, and Private High School sub‐purposes. Students in these sub‐purposes will generate different trip rates and will also have different trip lengths, as high school and college students typically travel farther to school than middle school and elementary students. To match the school age population on the production side, enrollment information was obtained for each school sub‐purpose. It was revealed that private school enrollment was about 10% of public school enrollment, thus the person trip rates were subsequently divided up into 90% public school‐based and 10% private school‐based.

Home‐Based Other
The HBO model structure is similar to HBShop. The demographic variables used for the HBO model are:
 Whether the person is a worker or not
 Household Size
 Household Income
 Number of autos in the household
The estimated person HBO trip rates and survey observations are shown below:

Table 19: HBOther Production Trip Rates
Worker or Not Household
Size
Household
Income
Household
Autos
Person
Trip Rate
Observations
0 1 [0, 50000] 0 1.011 36
0 1 [0, 50000] 1 1.208 220
0 1 [0, 50000] 2+ 2.163 41
0 1 >= 50K All 1.464 55
0 2 [0, 50000] 0 or 1 1.189 160
0 2 [0, 50000] 2+ 1.375 323
0 2 [50000, 100k] 0 or 1 1.037 65
0 2 [50000, 100k] 2+ 1.870 345
ATTACHMENT 1
47 Worker or Not Household
Size
Household
Income
Household
Autos
Person
Trip Rate
Observations
0 2 [100k, 150k] All 2.151 37
0 2 [150k, inf] All 1.771 74
0 3+ [0, 50000] 0 or 1 0.849 170
0 3+ [0, 50000] 2+ 1.801 557
0 3+ [50000, 100k] 0 or 1 1.280 61
0 3+ [50000, 100k] 2+ 1.965 899
0 3+ [100k, 150k] All 2.268 303
0 3+ [150k, inf] All 2.033 222
1 1 [0, 50000] 0 or 1 1.731 336
1 1 [0, 50000] 2+ 0.899 64
1 1 [50000, 100k] 0 or 1 1.245 128
1 1 [50000, 100k] 2+ 1.033 80
1 1 >= 100k All 2.699 38
1 2 [0, 50000] 0 or 1 0.996 80
1 2 [0, 50000] 2+ 1.125 339
1 2 [50000, 100k] 0 or 1 1.413 63
1 2 [50000, 100k] 2+ 0.927 661
1 2 [100k, 150k] All 1.353 204
1 2 [150k, inf] All 0.763 61
1 3+ [0, 50000] 0 or 1 1.333 122
1 3+ [0, 50000] 2+ 1.282 380
1 3+ [50000, 100k] 0 or 1 0.328 33
1 3+ [50000, 100k] 2+ 2.020 929
1 3+ [100k, 150k] All 2.070 299
1 3+ [150k, inf] All 1.587 202
There are a few observations in this estimation:
 Workers make more HBO trips in 1 person households when they are the only available shopper, but make less trips in two or more person households when there are other people available to make the HBO trip.
 Workers and non‐workers make the most HBO trips in 3+ person households and make the least trips in 1 person households
 Workers make more HBO trips in 0 or 1 auto households vs. households that have more than 1 available vehicle. Non‐Workers make more HBO trips when more than 1 vehicle is available.
 In general, more HBO trips are made as household income increases.

ATTACHMENT 1
48 There are a couple of highlighted anomalies. In these cases, the trip rate decreases when the household income increases. This could be caused by people at the top income levels reducing their HBO trips by consolidating their trips, as their value of time is higher to them.

Non Home‐Based Work
The demographic variables used for the NHBW model are:
 Household Size
 Household Income
 Number of kids in household under 18
The general rationale for these variables is:
 The larger the household size, the less trips due to substitution  The higher the household income, the more trips
 The more kids in the household, the more trips
The estimated person NHBW trip rates and survey observations are shown below:

Table 20: NHBW Production Trip Rates
Household
Size
Household
Income
Number of Kids
Under 18
Person
Trip Rate
Observations
1 [0, 50,000] All 0.967 400
1 [50,000, 100k] All 1.260 208
1 >= 100k All 1.972 38
2 [0, 50,000] All 0.658 419
2 [50,000, 100k] 0 1.157 674
2 [50,000, 100k] 1 or 2+ 0.875 50
2 [100k, 150k] All 0.526 204
2 [150k, inf] All 0.757 61
3+ [0, 50,000] 0 0.148 87
3+ [0, 50,000] 1 0.379 156
3+ [0, 50,000] 2+ 0.815 259
3+ [50,000, 100k] 0 0.555 210
3+ [50,000, 100k] 1 0.698 284
3+ [50,000, 100k] 2+ 1.089 468
3+ [100k, 150k] 0 0.473 45
3+ [100k, 150k] 1 1.221 90
ATTACHMENT 1
49 3+ [100k, 150k] 2+ 2.036 164
3+ [150k, inf] 0 1.329 58
3+ [150k, inf] 1 0.816 46
3+ [150k, inf] 2+ 1.549 98
Note: NHBW Rates only apply to full time and part time workers There are a few observations in this estimation:
 In general, higher income households generate more NHBW trips  In general, more kids in the household generate more NHBW trips  In general, larger household sizes equates to fewer NHBW trips for workers. This is likely due to other household members making the trip to substitute for the worker (e.g. another family member picking up a child from daycare instead of the worker on their way home from work).
 In general, more HBO trips are made as household income increases.

Non Home‐Based Other
The demographic variables used for the NHBO model are:
 Person Age
 Household Size
 Person Work Status
The general rationales for these variables are:
 Persons in the prime age categories (30‐50) make the most NHBO trips  Persons in the larger size households make more NHBO trips  Workers make fewer NHBO trips than non‐workers
The estimated person NHBO trip rates and survey observations are shown below:

Table 21: NHBO Production Trip Rates
Age Household
Size
Worker or Not Person
Trip Rate
Observations
[0, 17] 1 All 0.000 0
[0, 17] 2 All 0.547 60
[0, 17] 3+ 0 0.698 1621
[0, 17] 3+ 1 1.062 46
[18, 30] 1 All 0.438 176
[18, 30] 2 0 0.411 83
[18, 30] 2 1 0.475 268
[18, 30] 3+ 0 0.424 122
ATTACHMENT 1
50 Age Household
Size
Worker or Not Person
Trip Rate
Observations
[18, 30] 3+ 1 0.191 333
[31, 40] 1 All 0.777 129
[31, 40] 2 0 1.498 43
[31, 40] 2 1 0.520 258
[31, 40] 3+ 0 2.782 272
[31, 40] 3+ 1 0.973 825
[41, 50] 1 0 2.523 57
[41, 50] 1 1 0.570 144
[41, 50] 2 0 0.992 39
[41, 50] 2 1 0.592 400
[41, 50] 3+ 0 1.854 193
[41, 50] 3+ 1 0.641 641
[51, 64] 1 0 0.724 52
[51, 64] 1 1 0.907 181
[51, 64] 2 0 1.192 321
[51, 64] 2 1 0.759 570
[51, 64] 3+ 0 1.845 106
[51, 64] 3+ 1 0.559 307
65+ 1 0 0.731 262
65+ 1 1 0.694 75
65+ 2 0 0.667 673
65+ 2 1 0.505 107
65+ 3+ 0 0.522 122
65+ 3+ 1 0.127 26
There are a few observations in this estimation:
 The 31‐40 age category has the highest NHBO trip rates, followed by 41‐50, 51‐64, 65+, 0‐17, and 18‐30.
 Increases in household size leads to increased trip rates.  In almost every case, non‐workers have higher NHBO trip rates than workers. There is one exception for the 51‐64 age, 1 person household category. The cause of this may be that this age category is near retirement, and the worker may be more likely to be part time versus full time.

Results
Unbalanced trip production results are described below and are segmented by trip purpose.

The trip productions are also compared with the Base Year 2010 model and productions from the blended 2012 CHTS and 2017 NHTS. In general, the blended survey shows a reduced number of trips and is most likely due to an under rport of trips. The productions are in line with the 2010 model and generally show an increase in trips.

ATTACHMENT 1
51 Table 22: Model Trip Productions vs. Survey and 2010 Model HBW HBShop HBO
City Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
2010
Model
CHTS/
NHTS
Survey
Santa Barbara 93,301 87,522 61,081 35,913 32,512 46,416 163,288 154,756 163,782 Goleta 56,746 55,134 29,672 20,213 18,885 13,600 97,647 96,943 63,262 Santa Maria 103,223 85,592 60,952 45,105 36,217 35,137 209,581 192,370 114,671 Lompoc 31,163 28,703 35,066 13,339 11,546 18,587 60,642 60,721 74,089 Other 61,799 53,530 15,406 22,529 21,536 14,297 105,424 108,928 57,369

Total
346,232 310,482 202,177 137,099 120,694 128,037 636,582 613,719 473,173 HBSch NHBW NHBO
City Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
Previous
Base2010
Model
CHTS/
NHTS
Survey
Santa Barbara 30,849 32,177 29,406 57,449 60,003 44,522 70,601 65,156 77,715 Goleta 36,215 22,177 22,066 33,960 34,608 16,563 38,655 36,704 30,931 Santa Maria 43,761 45,910 25,893 58,861 57,703 23,092 94,648 82,530 72,452 Lompoc 13,146 14,539 13,496 16,881 18,469 10,552 27,290 26,578 28,774 Other 18,196 22,342 7,726 38,062 36,758 27,205 45,287 43,701 90,778

Total
142,167 137,146 98,586 205,214 207,541 121,934 276,481 254,669 300,650 Trip Attraction Models
The older SBCAG trip attraction model uses TAZ‐level estimates of employment by employment category and school enrollment as the main input. Trip rates by category and trip purpose were estimated from the CHTS. From the SBCAG Travel Model Final Report, the following rates were used by category and trip purpose:

ATTACHMENT 1
52 Table 23: Employment‐based Attraction Trip Rates
Special generators were also used to directly add in trip attractions by trip purpose. The inputs were directly estimated and no land use model was used in the estimation process. The trip rates were estimated from a Regional perspective and no local model rates were used in the estimation process. It is importation to note that trips represented in these trip rates represent person trips.

Trip production and attraction models have also been developed by the local cities (Santa Barbara, Goleta, Lompoc, and Santa Maria/Orcutt). These trip generation models are mostly land use based and thus use land use‐based inputs such as Commercial Floor Area Space instead of employees. These attraction models have been developed, calibrated and validated using the local characteristics of the city. In theory, they produce better estimates of local trips than the more generic employment‐based regional model. Therefore, the goal is to incorporate the local rates and models into the Regional trip attraction model. The local models were estimated and calibrated with more up‐to‐date and localized data than the Regional Model.

From an analysis of each local model, it was found that the trip rates for common land uses vary between the cities. The difference in trip rates probably reflects the local variations of trip generation. Thus, our objective was to retain the local variability as much as possible. This was done in order to capture local effects and to make the regional model results consistent with the local models. For each local city, its own attraction model was thus utilized. For example, for the TAZs in Lompoc, the Lompoc local model trip rates are applied while for the Santa Maria zones, the Santa Maria local model trip rates were applied. For the zones outside of the local cities, the current SBCAG Regional attraction model was used.

Land use inputs for the local model sections were transferred from the 2010 base year model.

Employment‐based inputs were generated from the 2015 InfoUSA database, which tracks the location of every business, their employment types, and the number of employees in that business. The InfoUSA database was reviewed by SBCAG staff and revised as necessary. The database was then aggregated to the TAZ zones to generate employment by employment type by TAZ. For future year forecasts, most of the land use and employment inputs are based upon Trip
Purpose Agr Com Ind Ser Off HH PreSch GradeSch HighSch PostSec Emp Emp Emp Emp Emp Enroll Enroll Enroll Enroll
HBW 1.131 0.360 2.751 1.300 0.360 - - - - -
HBShop - 1.674 - 0.118 - - - - - -
HBSchool - - - - - - - 1.452 1.165 0.448
HBO - 1.785 - 1.488 - 1.611 6.167 1.425 - -
NHBW 0.922 - 1.615 0.859 1.660 - - - - -
NHBO - 2.142 - 0.377 - 0.453 3.122 0.489 - -
ATTACHMENT 1
53 the results of the 2010 model derived UPLAN land use model that was then matched to the Regional Growth Forecast.

It is important to note that the local trip generation models include both trip production and trip attraction rates. For the Regional model, we only incorporate the trip attraction models. Trip production models are estimated based on population‐based trip rates estimated from the household travel surveys.

The table below describes all of the land uses specified in the local models. The table denotes the land use, the units of measure used for the land use, and the local models that use that land use variable. These local land use rates were unchanged from the 2010 base year model.

ATTACHMENT 1
54 Table 24: Local Model Landuses
Land Use Land Use Unit Santa Barbara Santa Maria Lompoc Goleta Rural Single Family Dwelling Units X X X X
Suburban Single Family Dwelling Units X X
Urban Single Family Dwelling Units X X
MF Zero Cars Rural Dwelling Units X    X
MF Zero Cars Suburban Dwelling Units X
MF Zero Cars Urban Dwelling Units X
MF 1 Cars Rural Dwelling Units X
MF 1 Cars Suburban Dwelling Units X
MF 1 Cars Urban Dwelling Units X
MF 2 Cars Rural Dwelling Units X
MF 2 Cars Suburban Dwelling Units X
MF 2 Cars Urban Dwelling Units X
MF 3 Cars Rural Dwelling Units X
MF 3 Cars Suburban Dwelling Units X
MF 3 Cars Urban Dwelling Units X
Rural MF Dwelling Units    X
Suburban MF Dwelling Units X
Urban MF Dwelling Units    X
Condos Dwelling Units X
Apartments Dwelling Units X
Mobile Homes Dwelling Units X X
Regional Commerce 1000 SF X X X
Neighborhood Commerce 1000 SF X X X
Agriculture Acres X X
Government Office 1000 SF X
Warehouse 1000 SF X
Commercial Services 1000 SF X
Entertainment 1000 SF X
Auto‐Related 1000 SF X X X
Restaurant 1000 SF X X X
Retail 1000 SF X
Lodging Rooms X X X X
Office 1000 SF X X X X
Institutional 1000 SF X X
Industrial 1000 SF X X
Hospital 1000 SF X X X X
Religious Facilities 1000 SF X X X
ATTACHMENT 1
55 Land Use Land Use Unit Santa Barbara Santa Maria Lompoc Goleta Police and Fire Services 1000 SF X X X
Elementary and Middle School Students X X X X
High School Students X X X
Colleges Students X X
Recreation (Parks and
Beaches)
Acres X X X X
Golf Acres X X
Church 1000 SF X
Light Industry 1000 SF X X
Heavy Industry 1000 SF X X
Medical Office 1000 SF X X X
Indoor Recreation 1000 SF X X
Public‐Quasi Public 1000 SF X
Service Station Service Stations X
Community College Students X
Special Generator Casino Slots    X
External Trip Distribution
Casino
Trips X
SBCAG_Agricultural Employment X
SBCAG_Industrial Employment X
SBCAG_Commercial Employment X
SBCAG_Office Employment X
SBCAG_Service Employment X
External to Internal Trips Trips X
Internal to External Trips Trips X
Research and Development 1000 SF X
Santa Barbara Airport Passengers/Day    X
UCSB Students X
Student Housing (Off Campus) Dwelling Units    X
Student Housing (On Campus) Dwelling Units X
La Cumbra Mall 1000 SF    X
Resort Hotel Rooms X
UCSB Faculty/Staff Employment    X
Fast Food Restaurant 1000 SF X
The trip attraction models estimate daily trip attractions by trip purpose. Generally, the local models use Home‐Based Work (HBW), Home‐Based Other (HBO) and Non‐Home Based (NHB) as their trip purposes. The Regional model, however, expands HBO into Home‐Based School (HBSchool), Home‐Based Shopping (HBShop), and HBO (Other trips that are not HBSchool and HBShop). In addition, the Regional model expands the Non‐Home Based purpose to Non‐Home ATTACHMENT 1
56 Based Work (NHBWork) and Non‐Home Based Other (NHBOther). The trip rates conversion utilizes certain factors to expand the trip purposes, which are discussed later in this section.

The Santa Barbara and Lompoc models use daily trip rates, while Goleta and Santa Maria/Orcutt use peak hour trip rates. Thus for Goleta and Santa Maria, factor conversions were applied to transform the peak hour trip rates to daily trip rates. The conversion factors are described later in this section.

Alongside the regular trip purposes, the local models include trip rates for internal‐external purposes and/or specific internal‐external trip rates. These purposes and trip rates model trips that come into and go out of the local model area. From a Regional model perspective, however, most of these trips stay internal to the County. Therefore, factors were used to separate out the internal portion of these trips from the “external to the County” portion.

Many of the local models include certain land uses as special generators (e.g. Casino, UCSB, Prison, etc.). The special generators portion of the Regional model models these trips, thus in general, these land uses and trip rates are included in the final attraction models. However, some of these land uses and rates are carried over in the conversion methodology, and the trips estimated from them are compared with the trips estimated from the special generators.

Given the points above, the trip rates and land uses of the local models were converted into the more generic land use trip rates and more expansive trip purposes using the following methodology:

1. Add the Internal‐to‐External (IX and XI) attraction rates to the internal attraction rates: In most of the local models, the trip purposes were separated into internal rates and external rates. The external rates estimate trips that come into or leave the local area. For the regional model purposes, many of these external trips would still remain within the county and be considered internal trips, thus we used the 2001 California Household Travel Survey (CHTS) and the 2003 Commuter Profile Survey (CPS) to determine by trip purposes and by local area the percentage of trips that stayed within the county versus the trips that left the county. We then applied this ratio to the IX rates and directly added them to the internal rates.

2. Standardized land use units: Certain land use units were converted into standard units in order to correctly aggregate the land uses. For example, the Santa Barbara model includes Golf Courses in Acre units and Recreational in ‘000 Square Feet units, both of which are aggregated to the Parks and Open Space land use. In this case, Golf Courses units and rates are converted into ‘000 Square Feet before aggregation is performed.

3. Expanded and collapsed trip purposes: Some trip purposes were collapsed into more aggregate purposes. For example, in Santa Barbara, the Golf and Recreation purposes are collapsed into the NHB trip purpose. Some other purposes were expanded. The Regional Model contains the HBW, HBShop, HBSchool, NHBWork, and NHBOther purposes, but the local models only includes HBW, HBO, and NHB. Thus, in most cases, HBO was expanded into HBShop, HBSchool, and HBO, and NHB was expanded into NHBOther and NHBWork. For HBShop and HBSchool, the land uses that were most pertinent to these purposes were extracted from HBO. Thus, for example, a majority of retail and commercial land uses were redirected from HBO to HBShop, and all ATTACHMENT 1
57 elementary, high school, and college enrollment rates were redirected from HBO to HBSchool. For the NHB purpose, rates were re‐distributed to NHBWork and NHBOther based upon ratios of HBW and HBO rates.

4. Collapse land uses to General Plan Categories and calculate revised trip rates: Revised trip rates by general plan category and trip purpose were calculated. The local models contained total land use data for the study areas, and this was used to compute total trips attracted for each general plan land use. The total trips were then divided by the total summed general plan land use units calculate the trip rate. The example below illustrates this technique:

5. Convert Vehicle Trip Rates into Person Trip Rates: In general, the local model trip rates were in vehicle trips. These were subsequently converted into person trip rates by applying auto occupancy factors that vary depending upon trip purpose. The occupancy factors were estimated from the 2001 CHTS.

6. Remove minor land uses that are covered by general categories or are covered by the special generators: There are several minor land use types in the local models, such as Airport Planes, Golf, etc. Many of these land use types have direct entries in the Special Generators list, which directly estimate their trip attractions. Some of these other land use types (e.g. Golf) are very minor and are included in the more generic land use types (e.g. Parks and Open Spaces). In order to simplify the attraction models, these land use types were removed and are handled either with special generators or with the more generic land uses. Removed land uses are noted below.

Initial trip rates by city and for the areas outside the cities were determined using the above methodology. During the calibration and validation stage, these rates were adjusted. They were adjusted to ensure that the variation of rates between cities and regions were not too large.

They were also adjusted to ensure that total trip attractions by city and purpose more closely matched trip productions and survey results.

The section below describes the local area attraction models including the land use and other variables used, and the final trip rates used. This section also describes how these rates and land uses are incorporated into the Regional Model.

Santa Barbara
The following trip rates were obtained for the City of Santa Barbara:

Table 25: Santa Barbara Final Land Use Trip Rates
Area Type Land Use HBW HBO HBShop HBSchool NHBWork NHBOther CBD Low Density Residential 0.00 0.51 0.00 0.00 0.00 0.53 CBD High Density Residential 0.00 0.39 0.00 0.00 0.00 0.39 CBD Low Density Commercial 7.28 20.19 5.62 0.00 5.42 11.60 CBD High Density Commercial 2.40 5.65 3.60 0.00 3.17 3.16 CBD Office 4.47 0.46 0.00 0.00 0.93 0.10
CBD Industry 2.67 0.00 0.00 0.00 0.71 0.00
ATTACHMENT 1
58 Area Type Land Use HBW HBO HBShop HBSchool NHBWork NHBOther CBD Institutional 6.23 8.72 0.00 0.00 1.72 2.51
CBD Elementary Student 0.14 0.00 0.00 1.31 0.00 0.03
CBD High School Student 0.09 0.00 0.00 0.46 0.00 0.01
CBD College 0.00 0.00 0.00 0.00 0.00 0.00
CBD Parks and Open Space 0.00 0.00 0.00 0.00 0.16 0.30
CBD Agricultural Employment 0.00 0.00 0.00 0.00 0.00 0.00 CBD Commercial Employment 0.00 0.00 0.00 0.00 0.00 0.00 CBD Industrial Employment 0.00 0.00 0.00 0.00 0.00 0.00 CBD Office Employment 0.00 0.00 0.00 0.00 0.00 0.00
CBD Service Employment 0.00 0.00 0.00 0.00 0.00 0.00
City Grid Low Density Residential 0.00 0.58 0.00 0.00 0.00 0.70 City Grid High Density Residential 0.00 0.39 0.00 0.00 0.00 0.39 City Grid Low Density Commercial 5.44 25.38 3.28 0.00 3.66 15.72 City Grid High Density Commercial 1.39 4.08 3.70 0.00 3.53 1.68 City Grid Office 6.26 0.64 0.00 0.00 1.55 0.17
City Grid Industry 2.82 0.00 0.00 0.00 0.75 0.00
City Grid Institutional 4.54 4.83 0.00 0.00 0.70 0.78
City Grid Elementary Student 0.15 0.00 0.00 1.38 0.00 0.06 City Grid High School Student 0.10 0.00 0.00 0.48 0.00 0.02 City Grid College 0.00 0.00 0.00 0.17 0.00 0.01
City Grid Parks and Open Space 0.00 0.00 0.00 0.00 0.38 0.32 City Grid Agricultural Employment 0.00 0.00 0.00 0.00 0.00 0.00 City Grid Commercial Employment 0.00 0.00 0.00 0.00 0.00 0.00 City Grid Industrial Employment 0.00 0.00 0.00 0.00 0.00 0.00 City Grid Office Employment 0.00 0.00 0.00 0.00 0.00 0.00 City Grid Service Employment 0.00 0.00 0.00 0.00 0.00 0.00 Suburban East Low Density Residential 0.00 0.66 0.00 0.00 0.00 0.71 Suburban East High Density Residential 0.00 0.48 0.00 0.00 0.00 0.42 Suburban East Low Density Commercial 5.80 38.22 3.23 0.00 3.79 23.98 Suburban East High Density Commercial 1.80 5.67 4.22 0.00 3.15 3.17 Suburban East Office 6.97 0.71 0.00 0.00 1.72 0.18
Suburban East Industry 3.14 0.00 0.00 0.00 0.83 0.00
Suburban East Institutional 4.57 8.10 0.00 0.00 1.17 2.18 Suburban East Elementary Student 0.17 0.00 0.00 1.54 0.00 0.07 Suburban East High School Student 0.11 0.00 0.00 0.54 0.00 0.03 Suburban East College 0.00 0.00 0.00 0.19 0.00 0.01
Suburban East Parks and Open Space 0.16 0.00 0.00 0.00 0.10 0.00 Suburban East Agricultural Employment 1.12 0.00 0.00 0.00 0.00 0.00 Suburban East Commercial Employment 0.70 0.00 0.00 0.00 0.00 0.00 Suburban East Industrial Employment 2.59 0.00 0.00 0.00 0.00 0.00 Suburban East Office Employment 1.21 0.00 0.00 0.00 0.00 0.00 Suburban East Service Employment 1.27 0.00 0.00 0.00 0.00 0.00 Suburban West Low Density Residential 0.00 0.66 0.00 0.00 0.00 0.71 Suburban West High Density Residential 0.00 0.47 0.00 0.00 0.00 0.42 Suburban West Low Density Commercial 5.93 20.20 3.80 0.00 4.17 12.22 ATTACHMENT 1
59 Area Type Land Use HBW HBO HBShop HBSchool NHBWork NHBOther Suburban West High Density Commercial 2.18 6.68 5.44 0.00 3.87 2.96 Suburban West Office 6.97 0.71 0.00 0.00 1.72 0.18
Suburban West Industry 3.14 0.00 0.00 0.00 0.83 0.00
Suburban West Institutional 3.17 7.34 0.00 0.00 0.75 1.81 Suburban West Elementary Student 0.17 0.00 0.00 1.54 0.00 0.07 Suburban West High School Student 0.17 0.00 0.00 0.54 0.00 0.03 Suburban West College 0.00 0.00 0.00 0.19 0.00 0.01
Suburban West Parks and Open Space 0.16 0.00 0.00 0.00 0.10 0.00 Suburban West Agricultural Employment 1.12 0.00 0.00 0.00 0.00 0.00 Suburban West Commercial Employment 0.70 0.00 0.00 0.00 0.00 0.00 Suburban West Industrial Employment 2.59 0.00 0.00 0.00 0.00 0.00 Suburban West Office Employment 1.21 0.00 0.00 0.00 0.00 0.00 Suburban West Service Employment 1.27 0.00 0.00 0.00 0.00 0.00 Lompoc
The daily trip attraction rates for the local Lompoc travel model are listed by trip purpose and land use unit below:

The final trip rates after calibration are shown in the table below:

Table 26: Lompoc Final Landuse Trip Rates
Land Use HBW HBO HBSchoo
l
HBSho
p
NHBWor
k
NHBOthe
r
Low density Residential 0.00 0.28 0.00 0.00 0.00 0.37
High Density Residential 0.00 0.19 0.00 0.00 0.00 0.24
Low Density Commercial 5.28 16.22 0.00 3.94 2.43 10.35
High Density Commercial 2.01 5.71 0.00 2.86 0.11 0.57
Industry 0.38 0.04 0.00 0.00 0.29 0.03
Institutional 2.19 3.76 0.00 0.00 0.39 0.67
Agricultural 0.78 0.05 0.00 0.00 0.10 0.01
Office 8.23 1.57 0.00 0.00 1.41 0.27
Elementary Students 0.12 0.00 1.45 0.00 0.01 0.05
High School Students 0.16 0.00 1.17 0.00 0.01 0.07
College Students 0.14 0.00 0.45 0.00 0.02 0.10
Airport Planes* 0.00 0.00 0.00 0.00 0.00 0.00
Jail Inmates* 0.00 0.00 0.00 0.00 0.00 0.00
Parks and Open Space 0.00 0.68 0.00 0.00 0.00 0.59
*Land uses removed and replaced with special generators Goleta
ATTACHMENT 1
60 The final daily Goleta trip rates after calibration are shown in the table below:

Table 27: Goleta Final Landuse Trip Rates
Land Use HBW HBO HBShop HBSchool NHBWork NHBOther
Low density residential 0.00 0.00 0.00 0.20 0.42 0.40
High Density Residential 0.00 0.00 0.00 0.40 0.29 0.37
Low Density Commercial 2.65 5.26 17.19 0.00 4.78 35.02
High Density Commercial 1.93 3.51 5.23 0.00 3.28 13.39
Agricultural 0.20 0.23 0.00 0.00 0.23 0.26
Industry 2.87 1.68 0.00 0.00 1.43 0.87
Institutional 1.34 2.60 0.00 0.00 1.06 2.06
Office 0.13 2.72 0.00 0.00 0.19 3.75
Parks and Open Space 0.27 1.02 0.00 0.00 0.34 1.30
Elementary Student 0.47 0.00 0.00 0.57 0.30 0.36
High School Student 0.33 0.00 0.00 0.45 0.28 0.38
UCSB 0.13 0.00 0.00 0.38 0.17 0.49
Student Housing 0.00 0.00 0.00 2.07 0.00 3.45
Student Housing Off Campus 0.00 0.00 0.00 2.07 0.00 3.45 Faculty/Staff 0.67 0.57 0.00 1.70 1.51 1.28
Resort Hotel** 0.00 0.00 0.00 0.00 0.00 0.00
SB Airport* 0.00 0.00 0.00 0.00 0.00 0.00
La Cumbra Mall 2.14 0.00 8.71 0.00 3.76 15.28
Golf** 0.00 0.00 0.00 0.00 0.00 0.00
*Land use removed and replaced with special generator
**Land use removed and replaced with more generic land use Santa Maria/Orcutt
The final daily Santa Maria trip rates after calibration are shown in the table below:

Table 28: Santa Maria Final Landuse Trip Rates
Land Use HBWA HBOA HBShop
A
HBSchool
A
NHBWork
A
NHBOther
A
Low density residential 0.00 0.00 0.00 0.00 0.00 0.80
High Density Residential 0.00 0.00 0.00 0.00 0.00 0.56
Low Density Commercial 3.18 6.30 4.39 0.00 1.46 3.99
High Density Commercial 2.01 6.97 1.80 0.00 2.37 3.76
Industry 0.87 0.49 0.00 0.00 0.18 0.07
Institutional 6.19 4.87 0.00 0.00 1.38 1.11
Office 5.61 4.34 0.00 0.00 0.98 0.57
Parks and Open Space 0.00 0.02 0.00 0.00 0.26 0.92
Elementary School 0.14 0.14 0.00 1.45 0.03 0.02
High School 0.09 0.61 0.00 1.17 0.09 0.05
ATTACHMENT 1
61 Land Use HBWA HBOA HBShop
A
HBSchool
A
NHBWork
A
NHBOther
A
College 0.00 0.00 0.00 0.45 0.10 0.00
Rooms 0.00 1.93 0.00 0.00 0.28 0.39
Service Stations* 0.00 0.00 0.00 0.00 0.00 0.00
*Land use removed and replaced with more generic land use type School Trips
In the above trip rate tables, it is important to note that enrollment inputs are broken down into elementary, middle, high school, college, private elementary, and private high school enrollment, and the elementary rates are applied to both the elementary and middle school enrollment numbers.

Results
Unbalanced trip attractions by city and for the entire region are shown below, compared with attraction results from the old SBCAG model for the year 2010 and attractions from the CHTS survey:

Table 29: Unbalanced Trip Attractions Comparison
HBW HBShop HBO
City Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
Previous
Base2010
Model
CHTS/
NHTS
Survey
Santa
Barbara 116,851 100,050 63,822 36,508 30,785 34,163 245,809 223,120 154,393 Goleta 52,121 53,388 27,464 32,464 28,876 26,105 92,719 82,735 53,054 Santa
Maria 90,329 78,279 48,552 44,186 38,133 33,840 131,573 110,779 99,639 Lompoc 21,788 18,517 14,761 11,218 9,627 15,920 51,147 43,450 60,879 Other 36,169 27,512 47,578 8,666 8,352 18,009 122,609 109,838 105,208 Total 317,259 277,747 202,177 133,041 115,773 128,037 643,857 569,921 473,173 HBSch NHBW NHBO
City Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
Previous
Base2010
Model
CHTS/
NHTS
Survey
Santa
Barbara 20,672 23,653 43,342 46,153 40,179 44,749 141,714 129,847 73,465 Goleta 84,124 70,883 4,700 38,991 45,514 17,815 98,587 117,562 34,730 Santa Maria 50,680 45,841 19,568 33,244 29,546 23,974 87,004 75,726 78,394 Lompoc 10,377 9,025 6,671 6,774 5,841 10,511 29,037 24,999 28,440 Other 20,152 19,985 24,306 34,127 27,841 24,886 30,346 29,061 85,621 ATTACHMENT 1
62 HBSch NHBW NHBO
City Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
Previous
Base2010
Model
CHTS/
NHTS
Survey Model
Previous
Base2010
Model
CHTS/
NHTS
Survey
Total 186,005 169,387 98,586 159,289 148,921 121,934 386,688 377,195 300,650 There are some places in some cities where the model results deviate from the survey and the older 2010 model. Overall, however, the attractions match with survey and 2010 model results.

Unbalanced attractions were also compared with unbalanced productions. The results are shown below:

ATTACHMENT 1
63 Table 30: Unbalanced Trip Productions and Attractions HBW HBShop HBO
City HBW_P HBW_A HBShop_P HBShop_A HBO_P HBO_A
Santa Barbara 93,301 116,851 35,913 36,508 163,288 245,809 Goleta 56,746 52,121 20,213 32,464 97,647 92,719
Santa Maria 103,223 90,329 45,105 44,186 209,581 131,573 Lompoc 31,163 21,788 13,339 11,218 60,642 51,147
Other 61,799 36,169 22,529 8,666 105,424 122,609

Total 346,232 317,259 137,099 133,041 636,582 643,857
HBSch NHBW NHBO
City HBSch_P HBSch_A NHBW_P NHBW_A NHBO_P NHBO_A
Santa Barbara 30,849 20,672 57,449 46,153 70,601 141,714 Goleta 36,215 84,124 33,960 38,991 38,655 98,587
Santa Maria 43,761 50,680 58,861 33,244 94,648 87,004
Lompoc 13,146 10,377 16,881 6,774 27,290 29,037
Other 18,196 20,152 38,062 34,127 45,287 30,346

Total 142,167 186,005 205,214 159,289 276,481 386,688
Overall, the productions match well with the attractions with some deviations.

Special Generators
After productions and attractions are calculated, special generator trips are added on both the production and attraction sides. Within the County there are various locations where trip generation and household and employment allocation are unique. In the travel model the trips generated from these special generators are predetermined.

UCSB, VAFB, the Prison, Chumash, and airports, etc. are "special generators" and are handled directly and separately in the travel model. Before the production and attraction models are run, employment, land use, households and population related to the special generator is removed from the socioeconomic inputs. A special generator table lists the generator and the unit amounts to remove from each category. Future projections of these special generators came from the original data sources, if available, and if not, from reasonable assumptions based on history and professional judgment. The following excerpt from the SBCAG 2005 Model Final Report explains how special generators were modeled.

ATTACHMENT 1
64 “The special generator analysis for the SBCAG region is largely based on guidelines from the previous SBCAG model. Before performing a special generator analysis, occupancy factors to convert vehicle trips to person trips were computed for each of the trip purposes. These are obtained from the survey data:

Table 31 Occupancy Factors
Trip Purpose Occupancy Factor
HBW 1.074 persons/vehicle
HB Other 1.750 persons/vehicle
NHB Other 1.717 persons/vehicle
NHBW 1.150 persons/vehicle
The various special generators are discussed below:
 Parks, Beaches and Missions:

The special generators for each of the parks, beaches and missions were based on the total number of vehicle trips obtained at these centers on a weekday. Following the procedure similar to the previous model, 75% of the vehicle trips are assigned to HBO trips and 25% of the vehicle trips assigned to visitor trips. The vehicle trips are converted to person trips and assigned to HBO attractions and Visitor Attractions based on occupancy factors. The results of the trip calculations by trip purpose are placed into an “SPLGEN” table. These trips are then added to the resulting trips of the trip generation model. Since some of these trips were generated by employment, an appropriate amount of employment was taken out of the demographics tables to ensure that no double‐counting took place.  Vandenberg Air Force Base:

The special generator productions and attractions for the air force base are based on the total number of employees. A factor of 0.85 vehicle trips/employee was used to compute the total number of trips. Based on the earlier model, 50% percent of the total vehicle trips were assigned to HBW trips and 50% were assigned to HB Other trips. The vehicle trips were converted to person trips by using occupancy factors and the number of person trips were split equally to yield the respective productions and attractions.

The special generator productions and attractions are added to the productions and attractions obtained earlier for each of the trip purposes for all zones, except for the zones containing the Vandenberg Air Force base.

In this Vandenberg case, the values in the special generator file replace the productions and attractions for that zone. This is done due to the fact that trip making patterns in an Air‐ ATTACHMENT 1
65 Force base differ widely from city trip patterns and the best method to capture this effect would be to conduct a separate trip analysis for the Air Force Base.”  U.S. Penitentiary:

The special generators was computed in exactly the same manner as the air force base with the difference being that a factor of 6 vehicle trips/employee (from the ITE Trip Generation Manual) was used to compute the total vehicle trips from the number of employees. This is in accordance with the previous version of the SBCAG model.  Commercial Airports:

The total vehicle trips available at the commercial airports were split in the ratio 2:1 to yield HBW and Visitor vehicle trips. The occupancy factors were applied and the person trips were assigned to HBW attractions and Visitor attractions respectively.  General Aviation Airports:

The total vehicle trips were equally split among HBO, NHB Work and NHB Other respectively.

The occupancy factors were applied and all the person trips were assigned to the respective attractions.
 Casino:

The procedure used was the same for the number of current and future number of employees.

In this model update Caliper revisited each of these special generators and deleted and added to this list as necessary. For example, UCSB and La Cumbre Mall were added. Caliper also used some of the local trip attraction variables from the local models to estimate tripmaking for the special generators. For example, for UCSB, the Goleta local model includes trip rates for UCSB students, students housed on‐campus, students housed off‐campus, and faculty/staff employees. A similar variable was added for La Cumbre Mall. For these two special generators, the information contained in the special generator table are the units of these variables. This is different than the other special generators which contain the final produced and attracted trips themselves. Caliper also revised the trip production and attraction methodology assumptions for other special generators where necessary. Finally, each special generator rate was updated with information for year 2015.

Internal‐External (IX) and External‐Internal (XI) Productions and Attractions After special generators are added, productions and attractions are calculated for internal‐ external trips coming into and leaving Santa Barbara County. For the IXXI model, several external zones were added to the model.

ATTACHMENT 1
66 The external zones in the zonal database in the Santa Barbara model comprise of 8 zones in San Luis Obispo County and 20 zones in Ventura County. The zones are aggregation of 2010 Census Blocks and were determined by district aggregations used for SLOCOG in San Luis Obispo and SCAG in Ventura.

To estimate the model, weighted IX and XI trips were extracted from the 2001 Statewide travel survey. For this purpose, trip chains from households in all the three counties were extracted from the survey dataset. The weights for Ventura County and San Luis Obispo County were estimated using a very similar procedure used in computing the weights for Santa Barbara County. The total number of trips computed is shown below in the Table below:

Table 32: Weighted IX Survey Trips
From County To County Weighted Trips
Santa Barbara Ventura/San Luis Obispo 43221
Ventura/San Luis Obispo Santa Barbara 45009
Weighted Trips from Survey (Directional)
For the purpose of estimation trips productions and attractions, the productions are interpreted as Trip ends occurring in Santa Barbara irrespective of the direction of the trip and the attractions are interpreted as trip ends in the external counties.

The configuration of the external zones is different from traditional transportation models. In traditional models, each external station consists of a single point or a node at the end of an external link, and traffic volume counts are available at each of those points. These traffic volumes become the de‐facto trip attractions. In this model, however, since the external zones extend beyond the county boundaries to district zones in SLO and Ventura, a sub‐model was developed to further distribute these traffic counts to the external zones. The extension of external zones past the external link allows for the calculation of the entire path from external zone to internal zone, and to derive complete times and distances. This enables the analysis of various jobs‐housing issues that exist in the region, and the costs associated with these external trip paths.

IX/XI Trip Productions and Attractions in SB County:

IX/XI trip productions and attractions were estimated based on demographics in Santa Barbara County. The key variables considered that determine IX/XI trips are the households and certain employment types in Santa Barbara and travel times from the Santa Barbara zone to the SLO and Ventura border. The time variables, called SLO_Time_to_SB and VEN_Time_to_SB, are introduced to the linear regression model. This variable reflects the travel time from the “edge” of Santa Barbara County to the respective TAZ and introduces a time component to the equation. Increased travel time is a disincentive to IX trip production, thus the resulting coefficient is negative, causing less trips to be produced from TAZs that are far away from the ATTACHMENT 1
67 external zones, and a better fit of survey data to the model. The time portion of the equation is based on a “gravity” decay function. A separate function is used for allocation SLO externals to SB internal zones, and Ventura externals to SB Internal zones. The entire allocation function is shown below:

Equation 1: SB Household Trip Productions from SLO to SB SLOTime odTripsSB SLO HH SLOTime e 2697.0 *0358.0 Pr *712.54*)*01.0( *    Equation 2: SB Employment Trip Attractions from SLO to SB SLOTime AttrTripsS SBLO ComEmp OffEmp ServEmp SLOTime e 2697.0 *0358.0 (*05.0( *712.54*) *      Equation 3: SB Household Trip Productions from Ventura to SB VENTime odTripsSB VEN HH VENTime e 3197.0 *0928.0 Pr *712.54*)*01.0( *    Equation 4: SB Employment Trip Attractions from VEN to SB VENTime AttrTripsV SBEN ComEmp OffEmp ServEmp VENTime e 3197.0 *0928.0 (*05.0( *712.54*) *      Where
HH = TAZ Households
ComEmp = Commercial Employment
OffEmp = Office Employment
ServEmp = Service Employment
VenTime = Weighted travel time from Ventura County border node to internal TAZ SLOTime = Weighted travel time from SLO County border node to internal TAZ In this equation, higher employment and household TAZs would attract more external trips, but so would zones that are closer to the county borders. For example, the effect of more employment in Carpinteria would outweigh the effect of more employment in Goleta in terms of attracting trips from Ventura.

IX/XI Trip Productions and Attractions in SLO and Ventura:

The basic attractions for each zone is calculated as (0.66 * HH + 0.36 * Total Employment).

These numbers are then multiplied by a factor that accounts for the distance of each zone from the nearest Santa Barbara zone. This factor is derived by using a curve‐fitting model based on the equations below. The attractions are then finally scaled to match the total attractions computed from the count data.

ATTACHMENT 1
68 San Luis Obispo:

TimetoSB K TimetoSB e *06796.03222.3 *)(*8925.0* 
Ventura:

TimetoSB K TimetoSB e *09728.09882.5 *)(*0601.1* 
where K is the K factor correction parameter for each zone and TimetoSB is the travel time from the external zone to the nearest Santa Barbara node. Thus the final trip generation equations for the external attractions are:

Equation 5: SLO HH Trip Productions
TimetoSB KHH TimetoSB e*06796.03222.3 *)(*8925.0***66.0   Equation 6: SLO Employment Trip Attractions
TimetoSB KEmp TimetoSB e*06796.03222.3 *)(*8925.0***36.0   Equation 7: Ventura HH Trip Productions
TimetoSB KHH TimetoSB e *09728.09882.5 *)(*0601.1***66.0   Equation 8: Ventura Employment Trip Attractions
TimetoSB KEmp TimetoSB e*09728.09882.5 *)(*0601.1***36.0  More trips are generated in external zones that have higher households and employment and in zones that are closer to the Santa Barbara border. The results are then normalized so that they sum up to the input IX trips.

The external trip productions and attractions are then balanced by the SLO and Ventura count estimates at the border links for the scenario year to ensure that the traffic flow going through the SLO and Ventura borders are consistent.

The ADT traffic counts are converted into commuter workers into and out of Santa Barbara County. The base model inputs for the IX‐XI model thus, are commuting workers from Ventura and San Luis Obispo into and out of SB County. The table below specifies the in‐commuting and out‐commuting worker estimates for the base year 2015 and future years. The estimates are from the Regional Growth Forecast:

ATTACHMENT 1
69 Table 33: In‐Commuting and Out‐Commuting Worker Estimates Commuter Type 2015 2020 2035 2045
SLO to SB In‐Commuters 10,863 12,036 13,998 16,787
Ventura to SB In‐Commuters 14,296 15,839 18,421 22,091
SB to SLO Out‐Commuters 8,930 8,930 8,930 8,930
SB to Ventura Out‐Commuters 2,433 2,433 2,433 2,433
Visitor Productions and Attractions
Productions
The visitor model for Santa Barbara County was largely based on a Santa Barbara County Visitor Survey in 2008 and from the earlier SBCAG model that used information from a visitor survey in Monterey County.

The visitor survey in Santa Barbara County yielded a total of 8.2 million annual or 23,000 daily (8.2 million / 365) visitors, with 61% being day trippers and 39% being non‐day trippers. In the previous model which was based on a 1999 visitors survey, there were about as many daily visitors, but only 20% of the visitors were day trippers while 80% of the visitors were non‐day trippers, signifying a shift towards day visitors. It is assumed that, on average, visitors who are not day trippers make about 4 trips per day, and day trippers make about 2.4 trips per day.

From the 2008 visitor’s survey, day trippers on average visited 1.2 venues which equates to 2.4 trips for the day. The 4 trip assumption for non‐day trippers is carried over from the current model. The number of trips per visitor was also estimated using the earlier model and was found to be in agreement with the assumptions. Thus the total number of visitor trips was estimated as:
(39*23000/100)*4 + (61*23000/100)*2.4 = 69,552 trips.

In the previous model, the estimate was 82,800 trips.

The explanatory variables that determine visitor productions are households, hotels/motels and the service employment. After considering the possible trip chains of visitors, it is assumed that on average one third of the trip productions are attributable to the service employment (where visitors move from one service area such as parks, restaurants to another service area). Further, the visitor survey indicated that out of the visitors who stayed either in hotels or with family, 50% stayed at hotels and 34% stayed with family members. In the previous survey, 56% stayed at hotels and 14% stayed with family members. Based on the above observations the model assumed for visitor productions was:

ATTACHMENT 1
70 Alpha*Households + Beta*Hotels/motels + Gamma*Service Employment with the constraints being:
 The total number of visitor trips equals 69,552.
 The term Gamma*Service Employment summed over all zones contributes to about 33% of the total trips.
 The term Alpha*Households summed over all the zones is approximately 1/1.65 of the term Beta*Hotels/Motels summed over all the zones.

The model obtained in the 2000 model was:

Visitor_P = 0.086*Households + 4.128*Hotels/Motels + 0.303*Service Employment.

Since there are less visitor trips in this estimation due to the higher percentage of day visitors, the coefficients of this model are now scaled down using a ratio of 69,552/82,800 or 0.84:

Visitor_P = 0.138*Households + 2.560*Hotels/Motels + 0.250*Service Employment.

Attractions
In order to estimate a visitor attraction model, the visitors attractions computed from the special generators (discussed in the next section) are subtracted from the total visitor trips. Thus the total number of visitor attractions must be equal to 63,940 (the total visitor trips) minus 13,600 (the number of visitor attractions from the special generator analysis, unchanged from the 2000 model), which yields 55,952 visitor attractions to be accounted for.

The key variables that determine visitor attractions are chosen to be households, service employment and commercial employment. Further, from the survey statistics, it was established that approximately 14.7 % of visitors visit homes of relatives during their stay. In the previous survey, 2.5% of visitors visit homes of relatives. The form of the attractions model is:

Alpha*Households + Beta*Service Employment + Gamma*Commercial Employment with the constraints being:
 The total number of visitor trips equals 69,552.
 14.7% of trips are connected to the household.

For the current model, the fitted equation was:

Visitor_A = 0.012*Households + 0.496*Service Employment + 0.336*Commercial Employment.

For the 2015 model, the attraction model given the updated constraints is:

Equation 9: Visitor Attractions
Visitor_A = 0.075*Households + 0.445*Service Employment + 0.301*Commercial Employment.

Balanced productions and attractions including IX‐XI and Visitor trips are shown in the following table.

ATTACHMENT 1
71 Table 34: Final Peak Balanced Productions and Attractions HBW_P HBW_A HBShop_P HBShop_A HBSch_P HBSch_A HBO_P HBO_A Santa Barbara 46,824 67,394 7,205 7,924 26,811 11,256 58,997 90,139 Goleta 27,534 27,377 3,962 7,053 23,228 59,619 34,596 33,656 Santa Maria 62,199 56,030 10,985 10,175 39,678 28,322 91,447 58,103 Lompoc 16,562 12,143 2,884 2,566 10,458 6,019 23,515 19,132 Other 32,856 23,031 5,009 2,328 15,376 10,336 40,897 48,421 External 0 0 0 0 0 0 0 0
Total 185,974 185,974 30,045 30,045 115,551 115,551 249,451 249,451 NHBW_P NHBW_A NHBO_P NHBO_A IXXI_P IXXI_A Visitor_P Visitor_A Santa Barbara 14,320 14,553 17,450 25,948 2,432 22,502 6,839 8,799 Goleta 8,244 11,756 9,359 18,020 580 4,085 2,396 4,792
Santa Maria 17,634 12,485 28,147 18,265 11,119 19,366 9,610 3,715 Lompoc 4,515 2,106 7,246 5,672 524 1,166 1,667 1,168
Other 10,036 13,850 12,080 6,377 2,464 14,475 3,299 5,337 External 0 0 0 0 61,594 17,118 0 0
Total 54,749 54,749 74,282 74,282 78,713 78,713 23,810 23,810 Table 35: Final Offpeak Balanced Productions and Attractions HBW_P HBW_A HBShop_P HBShop_A HBSch_P HBSch_A HBO_P HBO_A Santa Barbara 55,189 79,433 31,743 34,906 27,572 11,575 118,705 181,363 Goleta 32,453 32,268 17,455 31,073 23,888 61,312 69,608 67,718 Santa Maria 73,311 66,039 48,391 44,823 40,805 29,126 183,996 116,907 Lompoc 19,521 14,312 12,705 11,303 10,755 6,190 47,313 38,495 Other 38,726 27,145 22,066 10,255 15,813 10,629 82,286 97,425 External 0 0 0 0 0 0 0 0
Total 219,198 219,198 132,360 132,360 118,833 118,833 501,908 501,908 NHBW_P NHBW_A NHBO_P NHBO_A IXXI_P IXXI_A Visitor_P Visitor_A Santa
Barbara 48,488 49,277 59,763 88,867 6,357 58,820 23,422 30,134 Goleta 27,914 39,804 32,052 61,715 1,516 10,678 8,205 16,411 Santa
Maria 59,708 42,273 96,399 62,552 29,064 50,624 32,910 12,722 Lompoc 15,287 7,130 24,815 19,427 1,370 3,049 5,709 4,000 Other 33,982 46,895 41,372 21,841 6,440 37,838 11,298 18,278 External 0 0 0 0 161,009 44,748 0 0
Total 185,379 185,379 254,401 254,401 205,757 205,757 81,544 81,544 ATTACHMENT 1
72 Overall, the trip generation models estimate about 8.3 Home‐based Other trips per household,

2.17 Home‐based work trips per household, and about 11.3 total trips per household.

ATTACHMENT 1
73 Trip Distribution
Gravity Model
For trip distribution, the gravity model was implemented for the HBSchool, NHBOther, NHBWork, IXXI, and Visitor trip purposes. Destination choice models were applied to the other trip purposes where there was sufficient survey data and information on the home end of the trip for estimation.

Gravity Model Description
The following steps were used to obtain the gravity model for the SBCAG model. • Shortest paths were computed from zone to zone based on the generalized cost, and estimated congested travel times were skimmed from the least cost paths. Intra‐zonal travel times were computed based on the average time to the nearest 3 zones. • The balanced productions and attractions were obtained from the trip generation stage. • Friction Factor Computation: To compute the friction factors, the origin and the destination zone for each trip in the survey data was obtained using the analysis tools in TransCAD. The trip length for each trip was determined based on the shortest path matrix. Using the survey weights, the trip length frequencies were determined on a minute‐by‐minute basis for each of the trip purposes. The trip frequencies were plotted versus trip travel time intervals, and gamma function curves were fitted to match the observations. It was generally observed that the best fit was obtained by using two sets of gamma curves for each trip purpose. One curve was used to model the initial and the peak region of the observations (generally around 1 – 15 minutes) and the other curve was employed to model the tail region of the observations (> 15 minutes). The fitted curves and the coefficients are illustrated below. The trip interval used to aggregate trip frequencies was 1 minute for all trip purposes. The general form of the gamma friction factor curve employed is:

Equation 10: Friction Factor Equation
Time FF eTime * *)(*    
Where
 = Alpha parameter
= Beta parameter
 = Gamma parameter
ATTACHMENT 1
74 Different gamma parameters are used for different regions of time. A 1  value is used for the first time interval and a 2  value is used for the second time interval gamma function. The other parameters of the friction factor function are unchanged over the entire time interval. The separation of gamma parameters allowed for a closer fit of the model trip length frequencies to the survey. Custom parameters were developed for each trip purpose and each city by origin.

This allowed some regions such as Lompoc to model different trip lengths and different distributions from other regions such as Santa Barbara.

The estimated functions for all purposes and counties are listed below:

Table 36: Model Friction Factor Parameters
Period Purpose County Alpha Beta Gamma
< Time
Gamma
> Time
Time
Region
PK HBSch SantaBarbara 300000 3.802168 0.15 0.360486 11
PK HBSch Lompoc 300000 3.505745 0.15 0.288065 11
PK HBSch Goleta 300000 1.161519 0.15 0.095441 11
PK HBSch SantaMaria 300000 1.538287 0.15 0.1264 11
PK HBSch Other 300000 1.810912 0.15 0.148801 11
OP HBSch SantaBarbara 300000 2.83279 0.15 0.259226 11
OP HBSch Lompoc 300000 2.910705 0.15 0.230842 11
OP HBSch Goleta 300000 1.435587 0.15 0.113853 11
OP HBSch SantaMaria 300000 1.632405 0.15 0.129463 11
OP HBSch Other 300000 2.84636 0.15 0.225739 11
PK IXXI SantaBarbara 300000 0.801124 0.3 0.022426 24
PK IXXI Lompoc 300000 0.853051 0.3 0.02388 24
PK IXXI Goleta 300000 1.270493 0.3 0.035566 24
PK IXXI SantaMaria 300000 0.381484 0.3 0.010679 24
PK IXXI Other 300000 1.266186 0.3 0.035445 24
OP IXXI SantaBarbara 300000 5.25407 0.3 0.14708 24
OP IXXI Lompoc 300000 7.878652 0.3 0.220552 24
OP IXXI Goleta 300000 6.436259 0.3 0.180174 24
OP IXXI SantaMaria 300000 7.507094 0.3 0.21015 24
OP IXXI Other 300000 7.642334 0.3 0.213936 24
PK NHBO SantaBarbara 300000 1.566851 0.3 0.085406 13
PK NHBO Lompoc 300000 3 0.3 0.5 13
PK NHBO Goleta 300000 0.503514 0.3 0.03121 13
PK NHBO SantaMaria 300000 1.296362 0.3 0.39001 13
PK NHBO Other 300000 1.933936 0.3 0.605661 13
OP NHBO SantaBarbara 300000 2.479521 0.3 0.149316 13
OP NHBO Lompoc 300000 5 0.3 0.44 13
OP NHBO Goleta 300000 0.134429 0.3 0.046918 13
OP NHBO SantaMaria 300000 3.505334 0.3 0.294461 13
OP NHBO Other 300000 2.511262 0.3 0.668031 13
PK NHBW SantaBarbara 300000 0.843849 0.3 0.071233 16
ATTACHMENT 1
75 Period Purpose County Alpha Beta Gamma
< Time
Gamma
> Time
Time
Region
PK NHBW Lompoc 300000 4.5 0.3 0.2 16
PK NHBW Goleta 300000 0.011287 0.3 0.055595 16
PK NHBW SantaMaria 300000 0.582242 0.3 0.26049 16
PK NHBW Other 300000 0.018493 0.3 0.299222 16
OP NHBW SantaBarbara 300000 0.99394 0.3 0.409867 16
OP NHBW Lompoc 300000 0.087985 0.3 0.75696 16
OP NHBW Goleta 300000 0.017344 0.3 0.056987 16
OP NHBW SantaMaria 300000 0.352535 0.3 0.320887 16
OP NHBW Other 300000 0.003251 0.3 0.354812 16
PK VISITOR SantaBarbara 300000 0.025297 0.3 0.113443 13 PK VISITOR Lompoc 300000 0.115045 0.3 0.343458 13
PK VISITOR Goleta 300000 0.030136 0.3 0.087188 13
PK VISITOR SantaMaria 300000 0.07171 0.3 0.238445 13
PK VISITOR Other 300000 0.018868 0.3 0.158171 13
OP VISITOR SantaBarbara 300000 0.229741 0.3 0.279091 13 OP VISITOR Lompoc 300000 3 0.3 0.3 13
OP VISITOR Goleta 300000 0.088828 0.3 0.149138 13
OP VISITOR SantaMaria 300000 0.249007 0.3 0.163173 13
OP VISITOR Other 300000 0.91085 0.3 0.223117 13
The friction factors are inserted into the doubly‐constrained gravity model that takes the following function form:

Equation 11: Gravity Model
 

 
z zones all
)(
)(
izziz
ijjij
iij dfAK
dfAK PT (constrained to productions)
 

 
z zones all
)(
)(
zjzzj
ijiij
jij dfPK
dfPK AT (constrained to attractions)
where: Tij = the forecast flow produced by zone i and attracted to zone j
Pi = the forecast number of trips produced by zone i
Aj = the forecast number of trips attracted to zone j
Kij = the K‐Factor for flow between zone i and zone j
ij d = the impedance between zone i and zone j
ATTACHMENT 1
76 )( ij df = the friction factor between zone i and zone j In the gravity model, sparse use of K‐factors was applied. K‐factors are used to specifically capture trip patterns that cannot be captured using a generic gravity procedure. K‐factors were applied to school trips to bias TAZs to choose the nearest elementary, middle, or high school, depending upon the school subpurpose. For each TAZ, the TAZ of the nearest school depending on type was chosen, and that TAZ‐TAZ pair was positively biased in the K‐factor matrix. K‐factors were not used for College or Private School trips.

Destination Choice Model
For the HBW, HBO, and HBShop trip purposes, destination choice models were estimated for their trip distribution models. The following section summarizes the destination choice model estimation methodology and results for these purposes.

The destination choice model estimation and evaluation consisted of two steps:

1. Estimating one or more disaggregate destination choice models using the 2000 CHTS household survey data, and

2. Setting up the models for application at the aggregate (TAZ) level.

Destination choice models were estimated only for the home‐based trip purposes (HBW, HB Shop and HBO) since these trips are grounded at home at one end, it is intuitive to select the other end from a set of destinations. The gravity model is retained for the other purposes.

Estimation methodology
While destination choice models may appear simple in concept (i.e. a discrete choice model applied to a set of zones), they are unique in terms of the sheer number of alternatives that must be included in the choice set. It is therefore generally impractical to define such discrete choice models the traditional way, by creating a choice tree one alternative at a time as in traditional mode choice models. For this reason, TransCAD’s destination choice tools allow the modeler to create a generic destination alternative with a utility specification that will apply across all zones:

Figure 8: Destination Choice Modes
Since the utilities must be generic, they cannot involve an alternative specific constant. For the same reason, they cannot include origin attributes or socio‐economic characteristics since these data will be common across all alternative destinations (and hence cancel out of the calculation ATTACHMENT 1
77 of utility differences). Further, since the destination alternatives are aggregated from the disaggregate level (e.g., a shop, parking lot, office, etc.) to the zone level, a size variable must be considered to maintain consistency with TAZ‐level attractions (Ben‐Akiva and Lerman, 1985).

Sampling
The choice set of available destination zones for each trip in the survey was carefully crafted to improve the chance that the model fit choice decisions from amongst realistic options. If we assumed that all TAZs were available to every trip maker, the onus of explaining the observed choices would fall on the few relevant independent variables contained in the survey. A better approach is to generate a list of possible destination zones for each survey respondent, sampling from the set of all available zones.

Several sampling strategies suggest themselves as possibilities for destination choice. A reasonable assumption is to conduct importance sampling, assigning higher weights to the destinations that are deemed to be more likely to feature in the set. The sampling approach, however, might require a correction term to remove estimation bias. In addition, given the determination of sampled choice sets through Monte Carlo simulation, replications are necessary to account for the inherent stochasticity.

The total number of zones (1202) in the SBCAG model was well over the number of records in the household survey. While importance sampling of TAZs was attempted, this sparse coverage of chosen TAZs in the SBCAG household survey presented a unique challenge that required spatial aggregation to a higher level. This aspect is discussed next.

Zones vs. tracts
The SBCAG tracts layer presented a logical aggregation level to pool the TAZs into larger destinations. The 1202 TAZs were collected into 87 tracts, each with at least one TAZ being chosen in the survey. Estimation with this full set of tracts (without sampling) was thus feasible.

Model Results
This section describes the outputs of model estimation, including variables used, estimated coefficients and model fit.

Independent variables
The destination choice models made use of the following variables:

1. The size variable for each tract is the natural logarithm of the total number of trips attracted to the tract. The component TAZ‐level attractions were taken from the trip attraction model.

2. Weighted highway skims from origin TAZs to destination tracts. For a given origin TAZ, the skim to each destination tract was calculated as the average skim to all TAZs within the tract, weighted by the TAZ’s attraction. It should be noted that the weighted skims were calculated by trip purpose, since the attractions vary across purposes.

ATTACHMENT 1
78 3. Tracts were labeled as CBD, Urban or Suburban, based on the dominant type of zones within the tracts. Two dummy variables corresponding to the CBD and Urban categories were computed.

4. Various 4D measures were averaged across the zones within the tracts. These included transit stop density, retail employment density and retail + service employment density.

Estimated models
This section summarizes the destination choice model specifications and coefficients estimated.

Table 37: Destination Choice Utility Coefficients
Coefficient Description
B_Time Coefficient of travel time skim
B_CBD Coefficient of CBD dummy
B_Urban Coefficient of Urban dummy
B_TransitStops Coefficient of average transit stop density B_RetailEmp Coefficient of average retail employment density B_RetailServiceEmp Coefficient of average retail+service employment density B_Size Coefficient of size variable
B_IZDummy Coefficient of flag indicating Intrazonal choice B_IC_Dummy Coefficient of flag indicating intra‐city choice B_Lompoc_Dummy Coefficient of flag indicating Lompoc choice B_SM_Dummy Coefficient of flag indicating Santa Maria choice Table 38: Destination Choice HBW Model
Table 39: Destination Choice HBShop Model
Coefficient Estimate
B_Time ‐0.146224
Coefficient Estimate
B_Time ‐0.070549
B_CBD ‐1.560822
B_Urban ‐1.556641
B_TransitStops 0.016981
B_Size 0.837696
B_IZDummy 1.009743
B_IC_Dummy 0.5
B_Lompoc_Dummy 3.5
B_SM_Dummy 0.5
ATTACHMENT 1
79 B_CBD ‐3.057608
B_Urban ‐0.516672
B_RetailEmp 0.000719
B_Size 0.406336
B_IZDummy 0.981253
B_IC_Dummy 0.10
Table 40: Destination Choice HBO Model
Coefficient Estimate
B_Time ‐0.128385
B_CBD ‐1.154829
B_Urban ‐0.148428
B_RetailServiceEmp 0.000090
B_Size 0.528166
B_IZDummy 0.900381
B_TransitStops 0.0019935
B_IC_Dummy 0.25
Destination Choice Model Application
While the destination choice models for SBCAG were estimated from the disaggregate household survey, they are applied at the aggregate level. The broad goal is to distribute a vector of trip productions across destination zones. There were a couple of noted issues however, during the implementation stage:

1. The estimates did not weight the survey records, so the applied destination splits could potentially deviate from those in the survey.

2. The models were estimated to distribute zone productions across tracts (not zones).

A one‐step adjustment was executed to reduce the gap between the tract‐level totals and the corresponding size variables. This was achieved by correcting the utilities with the natural logarithm of the ratio of the size variable to the tract total.

Once the models were adjusted to replicate the destination splits at the tract level, the TAZ‐to‐ Tract flows were further split between the TAZs within the tract via a gravity‐type function (Ben‐ Akiva and Lerman (1985), chapter 9):

𝑃௢ሺ𝑑ሻ ൌ𝑀ௗ𝑒ିఈ ሺ௧௧೚೏ሻ
Where 𝑃௢ሺ𝑑ሻ is the probability that a trip originating in zone o will choose destination zone d; 𝑀ௗ is the size of zone d; 𝑡𝑡௢ௗ is the travel time between zones o and d; 𝛼 is a coefficient set to ATTACHMENT 1
80 two over the mean trip length. For a given origin zone o, the above probabilities over all zones d within a single tract will sum to unity.

A final utility correction was done to bring the zone‐level destination shares closer to those in the survey.

References

1. M. Ben‐Akiva and S. Lerman (1985) “Discrete Choice Analysis: Theory and Application to Travel Demand”. The MIT Press, Cambridge, MA.

Modeled trips were compared with survey trips based upon average trip lengths and trip length frequencies. City‐to‐city trip distributions were also compared.

Below is the comparison of survey versus modeled average trip lengths and travel times.

ATTACHMENT 1
81 Table 41: Survey vs. Model Trip Lengths and Travel Times Purpose Peak Distance Peak Time Offpeak Distance Offpeak Time Survey Model Survey Model Survey Model Survey Model
HBW 8.7 8.9 14.9 15.9 8.7 9.7 14.2 16.1
HBShop 3.1 4.3 11.0 10.0 5.6 4.5 10.2 10.1
HBSch 4.6 6.5 11.6 12.7 4.1 5.9 10.2 11.4
HBO 5.0 4.5 10.7 10.5 6.6 4.8 10.4 10.5
NHBW 7.5 7.1 13.9 13.9 6.1 5.5 12.2 11.4
NHBO 6.8 6.6 9.8 12.3 6.3 4.8 8.6 10.2
IXXI 35.6 28.1 46.4 36.2 29.2 25.4 38.9 32.3
Mode choice
The current mode choice model for SBCAG is an asserted model that divides the transit mode into 4 submodes: Walk‐to‐Local Bus, Drive‐to‐Local Bus, Walk‐to‐Express Bus, and Drive‐to‐ Express Bus. The model was asserted due to the lack of appropriate transit records in the survey datasets. For the model update, one of the objectives was to estimate mode choice models that include 4Ds parameters as part of the explanatory variables. A quick review found that no asserted models were available that included 4Ds parameters. Therefore, a full model estimation effort was required.

Independent variables, a priori hypotheses and estimation setup The CHTS household survey was analyzed to identify variables that might be useful in explaining mode choice behavior in the SBCAG area. In addition, various zonal and OD‐based skim variables were tagged to the survey in order to include the effects of geographic context, accessibility and network congestion.

At the zonal level, a CBD dummy was generated to capture the unobserved and perceived benefits of destinations in the Central Business District (CBD). Average transit stop densities were also computed at both the origin and destination ends of trips. Parking cost at the destination trip end was considered for the auto modes, with half the cost used for carpool trips. Congested highway and transit skims (including in‐vehicle and egress walk times) were tagged to the survey, making sure that AM and PM peak period trips used the skims from the appropriate time of day. Highway skims were used for the school bus mode.

One potential additional source of survey data was a transit on‐board survey processed by UC Santa Barbara (UCSB). Unfortunately, preliminary estimations combining these data with that in the household survey yielded unrealistic estimates. Subsequent discussions with UCSB staff ATTACHMENT 1
82 indicated that the on‐board survey was based on inadequate sampling and was not suitable for model estimation. The on‐board survey was therefore not included in the final estimation dataset. The CHTS dataset contained limited trip records that used transit as the mode of travel.

This data limitation meant that the transit mode could not be reliably broken down into sub‐ modes.

Summary of model estimation results
This section summarizes the final estimated model specifications and utility coefficients identified for various trip purposes. One objective was to estimate separate mode choice models for the peak and offpeak periods. However, only the HBW and HB Other trips were found to have variability across peak and off‐peak periods. A combined model was therefore estimated for each of the four other purposes (HB Shop, School, NHBW and NHBO).

The estimated models are a series of logit models (multinomial or nested) that vary by trip purpose and by peak/offpeak periods. For most purposes, the following travel modes are estimated:
 Auto drive alone
 Auto shared ride (carpool)
 Transit (bus)
 Walk
 Bike
As stated above, the limited sample sizes in the travel survey (particularly with Bus as the chosen mode) prevented a deeper nesting structure, the estimation of more sub‐modes, and the inclusion of variables such as transit fare. Asserted models give you the ability to define more detailed and complex models, however their parameters and coefficients may not be consistent with the survey observations.

A full complement of mode‐specific constants was estimated in each case, typically using the transit mode as the base. Where the t‐statistics of the constants were found to be low, they were still retained (at a lower confidence level) to maintain model estimation integrity in the face of missing variables.

In some cases, the Auto nest (comprised of Drive Alone and Carpool modes) was retained in the model while the corresponding logsum coefficient was fixed at 1. This was done because the estimation procedure did not identify a coefficient significantly different from 1.

Systematic utilities for the various modes are denoted by 𝑉஽஺(Drive Alone), 𝑉஼௉(Carpool), 𝑉஻௜௞௘, 𝑉ௐ௔௟௞, 𝑉ௌ஻(School Bus) and 𝑉஻௨௦.

The mode choice constants were subsequently re‐calibrated to better match the weighted shares observed in the household survey.

ATTACHMENT 1
83 Table 42: Mode Choice List of model coefficients
Coefficient Description
C_DA Mode‐specific constant for Drive Alone
C_CP Mode‐specific constant for Carpool
C_Walk Mode‐specific constant for Walk
C_Bike Mode‐specific constant for Bike
C_SB Mode‐specific constant for School Bus
B_Time Generic travel time coefficient
B_Time_DA Travel time coefficient for Drive Alone
B_Time_CP Travel time coefficient for Carpool
B_Time_Other Travel time coefficient for Transit and Non‐Motorized modes B_Egress Coefficient for transit egress time
B_CBD Coefficient for CBD dummy
B_Park Coefficient for auto parking cost at the destination zone B_O_Stops Coefficient for transit stop density at origin zone B_D_Stops Coefficient for transit stop density at destination zone B_AutoCost Coefficient for the auto operating cost
B_InitWait Coefficient for the transit initial wait time B_Fare Coefficient for the transit fare
B_IncLow Coefficient for low income origin zone
B_ORetail Coefficiient for 4Ds Retail employment within 0.5 mile of TAZ at the origin end B_DRetail Coefficiient for 4Ds Retail employment within 0.5 mile of TAZ at the destination end Theta (Auto) Logsum coefficient for Auto nest
Theta (Non‐Auto) Logsum coefficient for Non‐Auto nest
Many 4Ds variables were considered in the mode choice models. Of the 4Ds variables tested, only the transit stop density variable was ultimately included as the one having sufficient explanatory power and being statistically significant. The limited sample size of trip records in the survey may have played a role in the lack of 4Ds variables successfully included, while the CBD dummy showed greater explanatory power than other 4D variables tested.

Note that the bus mode is used as the base alternative with a mode specific constant of zero.

The nesting structure of each model, along with a list of each model variable and coefficient and rho‐squared result, is presented below:

ATTACHMENT 1
84 Figure 9: Mode Choice Peak HBW Model Structure
𝑉஽஺ ൌ𝐶஽஺ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚 ൅ 𝐵௉௔௥௞. 𝑃𝑎𝑟𝑘𝑖𝑛𝑔𝐶𝑜𝑠𝑡
𝑉஼௉ ൌ𝐶஼௉ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚 ൅ 𝐵௉௔௥௞.

𝑃𝑎𝑟𝑘𝑖𝑛𝑔𝐶𝑜𝑠𝑡
2 𝑉஻௨௦ ൌ𝐵்௜௠௘. 𝐵𝑢𝑠ூ௏்் ൅𝐵ா௚௥௘௦௦. 𝐸𝑔𝑟𝑒𝑠𝑠 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵ைௌ௧௢௣௦. 𝑆𝑡𝑜𝑝 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ሺ𝑂ሻ ൅𝐵஽ௌ௧௢௣௦. 𝑆𝑡𝑜𝑝 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ሺ𝐷ሻ ൅𝐵ூ௡௜௧்௜௠௘.𝐼𝑛𝑖𝑡𝑊𝑎𝑖𝑡𝑇𝑖𝑚𝑒
𝑉஻௜௞௘ ൌ𝐶஻௜௞௘ ൅𝐵்௜௠௘ . 𝐵𝑖𝑘𝑒 𝑇𝑖𝑚𝑒
𝑉ௐ௔௟௞ ൌ𝐶ௐ௔௟௞ ൅𝐵்௜௠௘ . 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒
Coefficient Estimate t‐statistic
C_DA 3.19000 24.80
C_CP 0.49100 3.80
C_Walk 1.80000 13.57
C_Bike ‐0.07500 ‐0.59
B_Time ‐0.07290 ‐46.74
B_InitTime ‐0.1300 ‐18.78
B_Egress ‐0.045500 ‐4.38
B_O_Stops 0.000898 2.17
B_D_Stops 0.00371 11.61
Theta (Auto) 1
Theta (Non‐Auto) 0.66 12.21
Rho_bar_squared 0.739
ATTACHMENT 1
85 Figure 10: Mode Choice Peak HB Other Model Structure 𝑉஽஺ ൌ𝐶஽஺ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஼௉ ൌ𝐶஼௉ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஻௨௦ ൌ𝐵்௜௠௘. 𝐵𝑢𝑠ூ௏்் ൅𝐵ா௚௥௘௦௦. 𝐸𝑔𝑟𝑒𝑠𝑠 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵ூ௡௜௧்௜௠௘.𝐼𝑛𝑖𝑡𝑊𝑎𝑖𝑡𝑇𝑖𝑚𝑒 𝑉஻௜௞௘ ൌ𝐶஻௜௞௘ ൅𝐵்௜௠௘ . 𝐵𝑖𝑘𝑒 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦
𝑉ௐ௔௟௞ ൌ𝐶ௐ௔௟௞ ൅𝐵்௜௠௘ . 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦

ATTACHMENT 1
86 Coefficient Estimate t‐statistic
C_DA 1.83000 13.24
C_CP 2.55000 18.42
C_Walk 0.78400 5.62
C_Bike ‐1.55000 ‐10.99
B_Time ‐0.02730 ‐46.66
B_Egress ‐0.2690 ‐15.39
B_InitTime ‐0.0845 ‐8.91
B_CBD 0.47200 14.93
Theta (Auto) 1
Theta (Non‐Auto) 0.90 4.42
Rho_bar_squared 0.489
Figure 11: Mode Choice HB Shop Model Structure
𝑉஽஺ ൌ𝐶஽஺ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚 ൅ 𝐵௉௔௥௞ . 𝑃𝑎𝑟𝑘𝑖𝑛𝑔 𝐶𝑜𝑠𝑡 ൅ 𝐵஺௨௧௢஼௢௦௧ .𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝐶𝑜𝑠𝑡 𝑉஼௉ ൌ𝐶஼௉ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚 ൅ 𝐵௉௔௥௞.

𝑃𝑎𝑟𝑘𝑖𝑛𝑔 𝐶𝑜𝑠𝑡
2 ൅𝐵஺௨௧௢஼௢௦௧ .𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝐶𝑜𝑠𝑡/2
𝑉஻௨௦ ൌ𝐵்௜௠௘ . 𝐵𝑢𝑠 𝐼𝑉𝑇𝑇 ൅ 𝐵ூ௡௜௧்௜௠௘.𝐼𝑛𝑖𝑡𝑊𝑎𝑖𝑡𝑇𝑖𝑚𝑒
𝑉஻௜௞௘ ൌ𝐶஻௜௞௘ ൅𝐵்௜௠௘ . 𝐵𝑖𝑘𝑒 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦
𝑉ௐ௔௟௞ ൌ𝐶ௐ௔௟௞ ൅𝐵்௜௠௘ . 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦 ൅ 𝐵ைோ௘௧௔௜௟ .𝑂𝑅𝑒𝑡𝑎𝑖𝑙 ൅𝐵஽ோ௘௧௔௜௟ .𝐷𝑅𝑒𝑡𝑎𝑖𝑙

ATTACHMENT 1
87 Coefficient Estimate t‐statistic
C_DA 2.22000 70.08
C_CP 1.63000 53.28
C_Walk ‐0.41500 ‐11.87
C_Bike ‐1.1800 ‐26.85
B_Time ‐0.03210 ‐25.18
B_Park ‐0.000963 ‐19.46
B_CBD 0.79800 14.44
B_AutoCost ‐0.04320 ‐24.72
B_InitTime ‐0.0685 ‐27.14
B_ORetail 0.000149 13.14
B_DRetail 0.000221 21.04
Theta (Auto) 1
Theta (Non‐Auto) 0.19 14.68
Rho_bar_squared 0.48
Figure 12: Mode Choice School Model Structure
𝑉஽஺ ൌ𝐶஽஺ ൅𝐵்௜௠௘஽஺ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஼௉ ൌ𝐶஼௉ ൅𝐵்௜௠௘஼௉ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஻௜௞௘ ൌ𝐶஻௜௞௘ ൅𝐵்௜௠௘ை௧௛௘௥ . 𝐵𝑖𝑘𝑒 𝑇𝑖𝑚𝑒
𝑉ௐ௔௟௞ ൌ𝐶ௐ௔௟௞ ൅𝐵்௜௠௘ை௧௛௘௥ . 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒
𝑉ௌ஻ ൌ𝐶ௌ஻ ൅𝐵்௜௠௘ை௧௛௘௥ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஻௨௦ ൌ𝐵்௜௠௘ை௧௛௘௥. 𝐵𝑢𝑠ூ௏்்

ATTACHMENT 1
88 Coefficient Estimate t‐statistic
C_DA 2.42000 5.90
C_CP 3.05000 8.10
C_Walk 3.04000 7.31
C_Bike 1.09000 2.69
C_SB 0 Fixed
B_Time_DA ‐0.03480 ‐4.84
B_Time_CP ‐0.04750 ‐3.12
B_Time_Other ‐0.03830 ‐2.05
Rho_bar_squared 0.24
Figure 13: Mode Choice NHBW Model Structure
𝑉஽஺ ൌ𝐶஽஺ ൅𝐵்௜௠௘஽஺ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஼௉ ൌ𝐶஼௉ ൅𝐵்௜௠௘஼௉ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஻௨௦ ൌ𝐵்௜௠௘ை௧௛௘௥. 𝐵𝑢𝑠ூ௏்் ൅𝐵ூ௡௜௧்௜௠௘.𝐼𝑛𝑖𝑡𝑊𝑎𝑖𝑡𝑇𝑖𝑚𝑒
𝑉ௐ௔௟௞ ൌ𝐶ௐ௔௟௞ ൅𝐵்௜௠௘ை௧௛௘௥ . 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦

ATTACHMENT 1
89 Coefficient Estimate t‐statistic
C_DA 3.41000 39.21
C_CP 1.98000 20.51
C_Walk 0.79700 9.36
B_CBD 1.79000 68.57
B_InitTime ‐0.20400 ‐21.20
B_Time_DA ‐0.02370 ‐12.06
B_Time_CP ‐0.02380 ‐12.29
B_Time_Other ‐0.0110 ‐35.00
Theta (Auto) 0.74600 10.36
Rho_bar_squared 0.58
Figure 14: Mode Choice NHBO Model Structure
𝑉஽஺ ൌ𝐶஽஺ ൅𝐵்௜௠௘஽஺ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚൅𝐵஺௨௧௢஼௢௦௧ .𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝐶𝑜𝑠𝑡 𝑉஼௉ ൌ𝐶஼௉ ൅𝐵்௜௠௘஼௉ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚 ൅ 𝐵஺௨௧௢஼௢௦௧ .𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝐶𝑜𝑠𝑡/2 𝑉஻௨௦ ൌ𝐵்௜௠௘ை௧௛௘௥. 𝐵𝑢𝑠ூ௏்் ൅𝐵ைௌ௧௢௣௦. 𝑆𝑡𝑜𝑝 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ሺ𝑂ሻ ൅𝐵஽ௌ௧௢௣௦. 𝑆𝑡𝑜𝑝 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ሺ𝐷ሻ 𝑉஻௜௞௘ ൌ𝐶஻௜௞௘ ൅𝐵்௜௠௘ை௧௛௘௥ . 𝐵𝑖𝑘𝑒 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦 𝑉ௐ௔௟௞ ൌ𝐶ௐ௔௟௞ ൅𝐵்௜௠௘ை௧௛௘௥ . 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦

ATTACHMENT 1
90 Coefficient Estimate t‐statistic
C_DA 5.22000 6.38
C_CP 5.79000 7.88
C_Walk 3.91000 5.43
C_Bike 0.48800 0.63
B_CBD 3.09000 7.69
B_O_Stops 0.00812 3.43
B_D_Stops 0.0106 4.47
B_AutoCost ‐0.14600 ‐1.32
B_Time_DA ‐0.02070 ‐0.22
B_Time_CP ‐0.06840 ‐1.18
B_Time_Other ‐0.0862 ‐4.69
Rho_bar_squared 0.51
Figure 15: Mode Choice IXXI Model Structure
𝑉஽஺ ൌ𝐶஽஺ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஼௉ ൌ𝐶஼௉ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஻௨௦ ൌ𝐵்௜௠௘. 𝐵𝑢𝑠ூ௏்் ൅𝐵ா௚௥௘௦௦. 𝐸𝑔𝑟𝑒𝑠𝑠 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵ூ௡௜௧்௜௠௘.𝐼𝑛𝑖𝑡𝑊𝑎𝑖𝑡𝑇𝑖𝑚𝑒 𝑉஻௜௞௘ ൌ𝐶஻௜௞௘ ൅𝐵்௜௠௘ . 𝐵𝑖𝑘𝑒 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦
𝑉ௐ௔௟௞ ൌ𝐶ௐ௔௟௞ ൅𝐵்௜௠௘ . 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦

ATTACHMENT 1
91 Coefficient Estimate t‐statistic
C_DA 1.83000 13.24
C_CP 2.55000 18.42
C_Walk 0.78400 5.62
C_Bike ‐1.55000 ‐10.99
B_Time ‐0.02730 ‐46.66
B_Egress ‐0.2690 ‐15.39
B_InitTime ‐0.0845 ‐8.91
B_CBD 0.47200 14.93
Theta (Auto) 1
Theta (Non‐Auto) 0.90 4.42
Rho_bar_squared 0.489
Figure 16: Off‐Peak HBW Model Structure
𝑉஽஺ ൌ𝐶஽஺ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஼௉ ൌ𝐶஼௉ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஻௨௦ ൌ𝐵்௜௠௘. 𝐵𝑢𝑠ூ௏்் ൅𝐵ூ௡௜௧்௜௠௘.𝐼𝑛𝑖𝑡𝑊𝑎𝑖𝑡𝑇𝑖𝑚𝑒
𝑉஻௜௞௘ ൌ𝐶஻௜௞௘ ൅𝐵்௜௠௘ . 𝐵𝑖𝑘𝑒 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦
𝑉ௐ௔௟௞ ൌ𝐶ௐ௔௟௞ ൅𝐵்௜௠௘ . 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦
Coefficient Estimate t‐statistic
C_DA 3.21 1.98
C_CP 0.917 0.57
C_Walk 1.22 0.73
C_Bike ‐0.613 ‐0.37
B_Time ‐0.0452 ‐3.29
B_InitTime ‐0.238 ‐1.22
B_CBD 1.74 3.07
Rho_bar_squared 0.68
ATTACHMENT 1
92 Figure 17:Off‐Peak HB Other Model Structure
𝑉஽஺ ൌ𝐶஽஺ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஼௉ ൌ𝐶஼௉ ൅𝐵்௜௠௘ . 𝐻𝑖𝑔ℎ𝑤𝑎𝑦 𝑆𝑘𝑖𝑚
𝑉஻௨௦ ൌ𝐵்௜௠௘. 𝐵𝑢𝑠ூ௏்் ൅𝐵ூ௡௜௧்௜௠௘.𝐼𝑛𝑖𝑡𝑊𝑎𝑖𝑡𝑇𝑖𝑚𝑒
𝑉஻௜௞௘ ൌ𝐶஻௜௞௘ ൅𝐵்௜௠௘ . 𝐵𝑖𝑘𝑒 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦
𝑉ௐ௔௟௞ ൌ𝐶ௐ௔௟௞ ൅𝐵்௜௠௘ . 𝑊𝑎𝑙𝑘 𝑇𝑖𝑚𝑒 ൅ 𝐵஼஻஽ . 𝐶𝐵𝐷 𝐷𝑢𝑚𝑚𝑦
Coefficient Estimate t‐statistic
C_DA 3.84 3.81
C_CP 4.05 4.02
C_Walk 2.44 2.38
C_Bike 0.665 0.64
B_Time ‐0.0204 ‐4.58
B_InitTime ‐0.0530 ‐0.69
B_CBD 0.309 0.88
Rho_bar_squared 0.42
Re‐calibration of constants
In this section, we present an analysis of the observed and modeled mode shares by trip purpose, after adjusting the mode specific constants to better replicate the shares indicated in the household survey.

Peak models
The following table shows the mode shares observed in the survey for peak‐period trips:

ATTACHMENT 1
93 Table 43: Observed Peak Mode Shares by Trip Purpose
HBW HBShop HBO HBSch NHBW NHBO
DA 85.7 56.5 29.7 8.6 78.8 28.7
CP 8.2 41.5 65.0 59.9 18.5 69.9
Bus 1.0 0.4 0.3 2.6 1.3 0.2
Bike 1.8 0.0 0.8 5.6 0.0 0.3
Walk 3.3 1.6 4.0 10.0 1.4 0.9
School Bus 0.0 0.0 0.0 12.9 0.0 0.0
Total 100.0 100.0 100.0 100.0 100.0 100.0
The corresponding shares from the SBCAG model are presented below, showing an accurate replication of the survey data:

Table 44: Model Peak Mode Shares by Trip Purpose
HBW HBShop HBO HBSch NHBW NHBO
DA 85.4 56.7 29.8 8.3 80.2 29.3
CP 8.1 41.7 65.1 60.0 17.4 68.5
Bus 1.0 0 0.3 2.8 0.4 0.9
Bike 1.9 0 0.8 5.9 0.0 0.3
Walk 3.6 1.6 3.9 10.3 2.1 1.0
School Bus 0.0 0.0 0.0 12.6 0.0 0.0
Total 100.0 100.0 100.0 100.0 100.0 100.0
Off‐peak models
The following table shows the mode shares observed in the survey for off‐peak‐period trips:

Table 45: Observed Offpeak Mode Shares by Trip Purpose
HBW HBShop HBO HBSch NHBW NHBO
DA 84.9 56.9 39.7 26.3 79.7 39.9
CP 9.9 37.8 53.4 33.7 12.4 54.8
Bus 0.4 2.7 0.6 1.7 1.4 1.5
Bike 2.1 0.6 1.6 5.1 0.0 0.4
Walk 2.7 1.9 4.8 19.2 3.7 3.4
School Bus 0.0 0.0 0.0 13.8 0.0 0.0
100.0 100.0 100.0 100.0 100.0 100.0
The corresponding shares from the SBCAG model are presented below, showing an accurate replication of the survey data:

ATTACHMENT 1
94 Table 46: Model Offpeak Mode Shares by Trip Purpose
HBW HBShop HBO HBSch NHBW NHBO
DA 84.6 57.0 40.0 25.6 80.8 39.6
CP 9.8 38.0 51.4 34.0 12.5 55.8
Bus 0.3 2.5 0.4 1.6 0.3 0.8
Bike 2.2 0.6 1.4 5.4 0.0 0.4
Walk 3.0 2.0 4.2 19.9 6.3 3.3
School Bus 0.0 0.0 2.7 13.6 0.0 0.0
100.0 100.0 100.0 100.0 100.0 100.0
ATTACHMENT 1
95 Time of Day
The PA to OD and time‐of‐day steps convert the peak and off‐peak production‐attraction matrices from mode split into eight time periods: AM, Late AM, Lunch, Early PM, PM, Evening, Late Evening, and Night. Together, the eight time periods make up the 24 hours of the day. The eight periods are as follows:

Table 47: Model Time Periods
Time Period Hours
AM 7am‐9am
Late AM 9am‐12pm
Lunch 12pm‐2pm
Early PM 2pm‐4pm
PM 4pm‐6pm
Evening 6pm‐8pm
Late Evening 8pm‐12am
Night 12am‐7am
Departure and return percentages were estimated for each time period from the 2001 CHTS travel survey. The estimated percentages are shown below:

Table 48: Trip Diurnal Factors by Trip Purpose
Time Period AM Late
AM
Lunch Early
PM
PM Evening Late
Evening
Night
Hours 7am‐9am 9am‐
12pm
12pm‐2pm 2pm‐4pm 4pm‐6pm 6pm‐8pm 8pm‐12am 12am‐7am
HBW Depart% 26.62% 6.03% 5.85% 2.07% 1.25% 0.50% 0.61% 9.99% HBW Return% 0.53% 1.99% 4.65% 5.94% 21.18% 7.85% 4.36% 0.59% HBShop Depart % 2.13% 13.64% 4.21% 6.68% 4.96% 4.34% 1.87% 0.74% HBShop Return % 0.54% 16.21% 9.32% 6.99% 14.61% 9.35% 4.31% 0.10% HBSch Depart % 43.29% 4.80% 1.19% 0.25% 2.17% 1.53% 0.14% 0.94% HBSch Return % 0.12% 1.35% 3.37% 29.07% 7.37% 1.64% 2.76% 0.00% HBO Depart % 13.57% 8.05% 3.84% 7.15% 8.17% 6.78% 0.97% 2.33% HBO Return % 4.10% 5.49% 4.60% 8.52% 11.12% 6.79% 8.08% 0.47% NHBW Depart % 5.41% 10.17% 7.56% 11.93% 8.55% 4.32% 1.41% 0.65% NHBW Return % 5.98% 11.92% 8.98% 10.74% 6.52% 2.78% 0.58% 2.50% IXXI Depart % 7.39% 9.51% 12.15% 9.80% 7.47% 1.99% 0.44% 1.25% IXXI Return % 6.39% 13.82% 10.90% 8.71% 5.05% 0.50% 0.33% 4.30% Visitor Depart % 4.97% 12.51% 6.24% 7.18% 9.28% 6.03% 1.06% 2.73% Visitor Return% 5.44% 11.31% 4.94% 10.54% 8.30% 1.98% 0.51% 7.00% In addition to applying the percentages, the person trips are converted into vehicle trips. For the drive alone model, there is a one‐to‐one correspondence between person trips and vehicle trips.

For the Shared Ride mode, average auto occupancy rates were estimated by trip purpose from the CHTS survey. These rates were applied to the SR trips to convert the person trip matrices into vehicle trip matrices. The occupancy rates are listed below:

ATTACHMENT 1
96 Table 49: HOV Occupancy Rates
Trip Purpose Occupancy Factor
HBW 2.15
HBShop 2.38
HBSch 2.12
HBO 2.39
NHBW 2.29
NHBO 2.34
IXXI 2.84
Visitor 3.23
After the daily and hourly OD trips are estimated, external to external trips are estimated and added to each trip matrix to create the final matrix. The external trips from and to San Luis Obispo and Ventura counties were extracted from the 2001 CHTS.

The following table lists the total vehicle OD trips estimated by the model by time period.

Table 50: Auto Vehicle Trips by Time Period
Time Period DA
Vehicle
Trips
SR Vehicle
Trips
Total
Vehicle
Trips
% of
Total
AM (2 hr) 134,992 57,252 192,243 13.90%
Late AM (3 hr) 178,335 60,293 238,629 17.25%
Lunch (2 hr) 138,979 43,441 182,420 13.19%
Early PM (2 hr) 155,011 52,823 207,834 15.02%
PM (2 hr) 174,950 74,126 249,076 18.00%
Evening (2 hr) 102,316 34,911 137,227 9.92%
Late Evening (4 hr) 63,939 24,807 88,746 6.41%
Night (7 hr) 70,530 16,815 87,345 6.31%
Total 1,019,052 364,468 1,383,520 1
% of Total 73.66% 26.34% 100.00%
The table shows a shift of trips from the peak periods (AM and PM) to offpeak (Late AM, Early PM, Evening). This shift is reflected in the traffic count distributions as well.

The estimated transit trips are shown below:

Table 51: Peak and Offpeak Transit Trips
Period Transit
Trips
Peak 6,656
Offpeak 15,897
Total 22,553
ATTACHMENT 1
97 The estimated all‐day walk and bike OD trips are shown below:

Table 52: All Day Walk and Bike Trips
Mode Trips
Bike 28,453
Walk 89,812
Total 118,265
Truck Model
A simplified truck model was inserted into the model stream to estimate Internal‐to‐Internal truck trips. IX truck trips and XX truck trips are already factored into the model since the IX and XX trips are based on external station traffic counts that include truck trips. The truck model is based on SCAG’s RTP 2003 truck model, which estimates truck trip rates based upon employment variables. The original SCAG truck trip rates are shown below:

Table 53: SCAG 2003 Truck Trip Rates
Category Daily Truck Trip Rates
Light Trucks Medium Trucks Heavy Trucks
Households 0.0390 0.0087 0.0023
Ag/Min/Construction 0.0513 0.0836 0.0569
Retail 0.0605 0.0962 0.0359
Government 0.0080 0.0022 0.0430
Manufacturing 0.0353 0.0575 0.0391
Transportation/Utility 0.2043 0.0457 0.1578
Wholesale 0.0393 0.0650 0.0633
Other 0.0091 0.0141 0.0030
The employment categories were re‐categorized into the SBCAG employment categories, and the trip rates were then re‐estimated based upon the SBCAG employment categories:

Table 54: SBCAG Truck Trip Rates
Category Daily Truck Trip Rates
Light Trucks Medium Trucks Heavy Trucks
Households 0.0390 0.0087 0.0023
Agriculture+Industry 0.0422 0.0688 0.0468
Service 0.0095 0.0023 0.0162
Commercial 0.0380 0.0608 0.0281
Truck trip generation, distribution, and time‐of‐day models were added to the model stream.

The truck distribution model utilized a Gravity model where light, medium, and heavy friction ATTACHMENT 1
98 factors were transferred over from the SCAG model. The time of day diurnal factors were also transferred over from the SCAG model. The friction factor values for the first 30 miles are shown below:

Table 55: Truck Friction Factors
Distance
(miles)
Truck Friction Factors
Light
Truck
Medium
Trucks
Heavy
Trucks
1 658,023 2,443,409 66,999
2 147,817 1,192,732 32,358
3 61,635 776,298 21,138
4 33,106 568,417 15,627
5 20,388 443,950 12,363
6 13,708 361,185 10,209
7 9,792 302,245 8,683
8 7,311 254,858 7,547
9 5,647 218,312 6,669
10 4,480 189,344 5,970
11 3,632 165,878 5,402
12 2,997 146,532 4,929
13 2,511 130,347 4,531
14 2,130 116,640 4,191
15 1,827 104,910 3,897
16 1,583 94,780 3,641
17 1,382 85,965 3,416
18 1,216 78,240 3,216
19 1,078 71,429 3,038
20 960 65,393 2,878
21 860 60,017 2,734
22 775 55,208 2,603
23 700 50,889 2,484
24 636 46,998 2,375
25 580 43,479 2,275
26 530 40,288 2,183
27 486 37,387 2,097
28 448 34,742 2,018
29 413 32,325 1,945
30 382 30,113 1,877
The truck time‐of‐day diurnal factors are shown below:

ATTACHMENT 1
99 Table 56: Truck Diurnal Factors
Time Period AM Late
AM
Lunch Early
PM
PM Evening Late
Evening
Night
Hours 7am‐
9am
9am‐12pm 12pm‐2pm 2pm‐4pm 4pm‐6pm 6pm‐8pm 8pm‐12am 12am‐7am Light Truck Depart% 5.73% 11.55% 7.70% 6.54% 5.38% 3.37% 2.75% 6.98% Light Truck Return% 5.73% 11.55% 7.70% 6.54% 5.38% 3.37% 2.75% 6.98% Medium Truck Depart% 4.77% 9.77% 6.52% 6.55% 6.58% 4.21% 3.69% 7.92% Medium Truck Return% 4.77% 9.77% 6.52% 6.55% 6.58% 4.21% 3.69% 7.92% Heavy Truck Depart% 3.87% 7.28% 4.85% 4.85% 4.85% 4.24% 7.25% 12.82% Heavy Truck Return% 3.87% 7.28% 4.85% 4.85% 4.85% 4.24% 7.25% 12.82% The estimated daily truck trips by trip type is listed in the following table:

Table 57: Truck Trips by City
Light
Trucks
Medium
Trucks
Heavy
Trucks
Total
Santa Barbara 3,285 2,523 1,639 7,447
Goleta 1,776 1,346 923 4,044
Santa Maria 2,826 2,234 1,364 6,424
Lompoc 785 432 299 1,516
Other 1,778 1,375 851 4,004
Total 10,450 7,909 5,076 23,436

The estimated truck trips by time period is listed in the table below:

Table 58: Truck Vehicle Trips by Time Period
Time Period Truck Vehicle
Trips
AM (2 hr) 2,126
Late AM (3 hr) 4,261
Lunch (2 hr) 2,841
Early PM (2 hr) 2,619
PM (2 hr) 2,398
Evening (2 hr) 1,623
Late Evening (4 hr) 1,695
Night (7 hr) 3,606
Total 21,169
ATTACHMENT 1
100
Highway and Transit Trip Assignment
In the highway assignment step, trips from the OD matrix are assigned to the network to determine volume of traffic flow on the links. In the SBCAG model, four assignments are performed: one for daily trips, one for AM peak hour trips, one for PM peak hour trips, and one for Midday hour trips. The Bi‐Conjugate User Equilibrium (UE) method is used for all of these assignments. This method has the stated goal that the volumes are estimated such that no user can improve his or her travel time from their origin to destination by choosing a different path than the one assigned to them.

The UE method works by first assigning all trips to the shortest routes based upon free flow travel time. Based on the volume assigned to each link, a congested travel time is estimated based on the follow delay function:

Equation 12: BPR Volume Delay Function
TIME = FFTIME * (1 + ALPHA*(Volume/Capacity)^BETA
Where:

TIME   = Estimated congested time
FFTIME   = Free‐flow travel time
ALPHA   = Parameter, usually between 0.1 and 2
BETA   = Parameter, usually between 3 and 10
Volume   = Assigned volume on link
Capacity = Estimated capacity of link
As a default, the model is run to either a maximum of 100 iterations per assignment period or a relative gap of 1e‐4, whichever comes first. Most assignments for SBCAG converge in about 30‐ 40 iterations in the most congested time periods.

Model Performance
The following model performance criteria were used to calibrate the model:
 Graphically display a scattergram of model daily volumes versus average annual daily traffic (AADT). The volumes should be in line with the AADT, with few outliers  Match Vehicle Miles Travelled (VMT) with Highway Performance Monitoring Study (HPMS) VMT estimates
 Calculate the correlation and R2 between model volumes and counts. R2 values should be greater than 0.88.
 Compare model flows versus counts by functional class and area type. Model volumes should be within certain standard percentage ranges of counts within the groupings  Compute Root Mean Square Error (RMSE) between volumes and counts. Volumes should be within certain stand RMSE values within the groupings.

ATTACHMENT 1
101
The following figure displays a scattergram of model volumes versus average annual daily traffic (AADT):

Figure 18: AADT vs. Model Volume Scattergram
Overall, the model volumes are fairly close to the counts with a few outlying exceptions.

The next table shows the model VMT and VHT by functional class for the base year:

Table 59: VMT and VHT by Functional Class: Base Year
Functional Class VMT VHT %VMT %VHT
Freeways 4,902,820 76,122 48.73% 37.25%
Expressways 0 0 0.00% 0.00%
Principal Arterial 1,105,560 24,877 10.99% 12.17%
Minor Arterial 2,161,402 48,083 21.48% 23.53%
Urban Collector 553,281 13,095 5.50% 6.41%
Rural Collector 205,227 5,715 2.04% 2.80%
Local 285,972 8,887 2.84% 4.35%
Centroid Connector 610,863 20,362 6.07% 9.96%
Ramp 208,554 6,515 2.07% 3.19%
System to System Ramp 25,917 671 0.26% 0.33%
HOV 636 10 0.01% 0.00%
Total 10,060,232 204,337 100.00% 100.00%
By comparison, the 2015 California Highway Performance Monitoring Survey (HPMS) estimates

9.81 million VMT for Santa Barbara County. The model estimate is a little higher than the HPMS estimate.

ATTACHMENT 1
102
Three other aggregate statistics were also calculated when comparing the daily counts versus flows: correlation, coefficient of determination, or R2 , and the calculation of Percent Root Mean
Square Error.

The R2 value between daily counts and flows was 0.97. The rule of thumb is that this value should be greater than 0.88 when conducting a regionwide comparison.

The equation of Percent Root Mean Square Error (%RMSE) is as follows:

Equation 13: RMSE Equation
100*
( / )
( /() )1
%
2 
  

j
j
j
j j
Count Numberofco unts
Model Count Numberofco unts
RMSE
The next table compares model volumes versus daily counts by functional class:

Table 60: Daily Model Volumes vs. Counts
Link Type Segments TotalCount TotalFlow PercentDif Standard %RMSE RMSE Standard Freeways 180 5,315,228 5,557,164 4.55% 6.00% 17.42% 25.00% Expressways 0 6.00% 0.00% 25.00%
Major Arterials 93 1,589,377 1,591,039 0.11% 10.00% 37.03% 40.00% Minor Arterials 199 552,305 426,847 ‐22.72% 10.00% 42.66% 50.00% Urban Collectors 118 1,823,803 1,723,191 ‐5.51% 20.00% 63.73% 60.00% Rural Collectors 24 77,689 48,731 ‐37.27% 20.00% 63.10% 60.00% Local Roads 21 64,804 39,737 ‐38.68% 88.70%
All Roads 635 9,457,286 9,427,458 ‐0.31% 32.63% 35.00%

The next table compares model flows with counts on a district‐by‐district level for the base year:

Table 61: Daily Model Volumes vs. Counts by District
District Segments TotalCount TotalFlow PercentDif %RMSE Santa Barbara 182 3,166,556 3,068,782 ‐3.09% 30.41%
Goleta 59 1,310,216 1,301,190 ‐0.69% 23.66%
Santa Maria 142 1,894,389 1,804,908 ‐4.72% 43.11%
Lompoc 45 369,724 383,613 3.76% 43.69%
Remaining SB County 203 2,481,519 2,624,058 5.75% 31.70% External 8 234,882 244,908 4.27% 6.20%
The next table and chart compare model flows with counts on a screenline‐by‐screenline basis for the base year:

ATTACHMENT 1
103
Table 62: Daily Model Volumes vs. Counts by Screenline
Screenline Segments TotalCount TotalFlow PercentDif %RMSE ARROYO BURRO 2 130,100 130,921 0.63% 1.07%
LOMPOC 4 38,300 36,840 -3.69% 21.30%
N SANTA MARIA 4 59416 58195 -2.06% 45.68%
NORTH GOLETA 4 62,796 55,301 -11.86% 22.84%
ORCUTT 4 20,290 22,763 12.19% 24.13%
SANTA MARIA 6 134,671 134,614 -0.04% 28.75%
SB AIRPORT 6 91,434 101,983 11.54% 17.43%
SB DOWNTOWN 6 117,889 106,981 -9.25% 31.24%
SOUTH GOLETA 7 158,884 161,544 1.67% 25.35%
VANDENBERG 6 37,500 40,749 8.66% 26.64%
WEST 101 3 41,500 45,982 10.80% 26.07%
WESTMONT 4 103,400 118,802 14.90% 30.63%
Figure 19: Count vs. Flow Comparison by Screenline
The following chart shows the deviation between screenline flows vs. counts with a curve showing the maximum recommended deviation based upon base volumes:
0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000

ARROYO BURRO
LOMPOC

N SANTA MARIA

NORTH GOLETA
ORCUTT

SANTA MARIA

SB AIRPORT

SB DOWNTOWN

SOUTH GOLETA

VANDENBERG
WEST 101

WESTMONT
Count or Flow
Screenline
Count vs. Flow Comparison by Screenline
Model Volume Count
ATTACHMENT 1
104
Figure 20: Deviations in Screenline Volumes
The following figures show AM and PM peak assignment volumes and congestion levels for Santa Barbara and Santa Maria:
0 0.1

0.2 0.3

0.4 0.5

0.6 0.7
0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 Deviation (Percent)
Screenline Count
Deviation in Screenline Volumes
Volume Deviation
ATTACHMENT 1
105
Figure 21: AM Flow and Congestion in South Santa Barbara Figure 22: PM Flow and Congestion in South Santa Barbara ATTACHMENT 1
106
Figure 23: AM Flow and Congestion in North Santa Barbara Figure 24: PM Flow and Congestion in North Santa Barbara ATTACHMENT 1
107
For transit assignment, SBCAG uses TransCAD’s Pathfinder method, the same method used for transit skimming. In the Pathfinder method, generalized cost is minimized. Generalized cost is computed using weighted values of in‐vehicle, access, egress, transfer, dwelling, and waiting times and other costs such as transfer penalty costs and fares. Pathfinder also performs route combinations, which reduces effective initial waiting times and assigns trips based upon the relative frequencies of the routes combined. Routes are combined if they serve the same origin‐ destination pair and if their travel times are relatively close to each other.

Peak and off‐peak transit trips are assigned separately. A post‐process routine aggregates these four assignments into a total transit flow table.

The following table lists the total estimated model ridership compared with daily estimated ridership for the base year 2015 and the ridership from the previous model’s base year 2010.

Daily ridership was estimated by dividing annual ridership by 300.

Figure 25: Modeled Transit Boardings vs. Ridership by Company Company Model
Boardings
Ridership
2015
Ridership
2010
Notes
Amtrak 12
BREEZE BUS 639 227 163
COASTAL EXPRESS 618 1,038 951 Vista in 2010
COLT 1,178 480 838
CUYAMA TRANSIT 0 5 Not in 2010
Central Coast Shuttle 32
Chumash Casino Resort 18
Clean Air Express 238 600 547
Greyhound 13
Guadalupe Flyer 116 247 381 Smooth in 2010
Guadalupe Shuttle 51 92
SBMTD 20,677 23,855 25,621
SLO RTA 18
SMAT 4,930 2,718 3,548
SYVT 603 143 176
Santa Barbara Airbus 0 3

WINE COUNTRY

EXPRESS
0 20 34
TOTAL 29,143 29,425 32,263
Feedback
After the end of the highway assignment step, the congested travel times are “fed back” into the both the highway and transit networks. Both the highway and transit skimming routines then use these congested times to produce congested highway and transit skim matrices. The ATTACHMENT 1
108
logic of feedback is that these congested time matrices are a more accurate measure of travel time than the initial free flow times, and thus would the downstream trip distribution and mode split steps would have more realistic input data. All downstream models are invoked again, ending with highway assignment and another set of congested times. This loop continues until a set number of feedback iterations are completed. The Multiple Successive Averages (MSA) method is used to calculate the “congested” time that is fed back into the network. A total of 5 feedback loops are performed.

ATTACHMENT 1
109
Sensitivity Analysis
To test the sensitivity of the model, several model inputs and parameters were changed. The model input changes included:
 Add 1000 households to a zone
 Double all households in entire county
 Add 1000 employment in Santa Ynez
 Add 1,000,000 sqft of low density commercial space to a zone in Lompoc  Double transit frequencies
 Triple parking costs
 Improve Arterial and Collector free flow speeds by 10 mph  Double auto operating costs from 19.93 cents/mile to 39.86 cents/mile to mimic higher gas prices
 Double value of time from $7.05/hr to $14.10/hr
 Improve household and employment diversity balance to all zones The following table summarizes the vehicle mile, vehicle hour, and other performance measure changes given the model input changes:

Table 63: Sensitivity Results Table 1
Performance Measure Base Add 1000
HH in SB
Add 1000
Low
Density
Commercial
Add 1000
Employment
in Santa
Ynez
Double Auto
Operating
Cost
Vehicle Trips 1,383,520 ‐0.26% ‐0.54% ‐0.52% ‐0.90%
Vehicle Miles (Total) 10,112,487 ‐0.37% ‐1.72% ‐0.40% ‐3.55% Vehicle Hours (Total) 213,669 ‐0.46% ‐2.08% ‐0.59% ‐2.23% Vehicle Miles/Vehicle Trips 7.31 ‐0.11% ‐1.18% 0.12% ‐2.67% Vehicle Hours/Vehicle Trips 0.154 ‐0.21% ‐1.55% ‐0.07% ‐1.34% Vehicle Miles/Capita 22.811212 ‐0.59% ‐1.72% ‐0.40% ‐3.55% Vehicle Miles/Commercial KSF 210.185204 ‐0.37% ‐3.72% ‐0.40% ‐3.55% Average Travel Distance (w/o XI) 6.117287 ‐0.12% ‐1.05% 0.28% ‐2.22% Average Travel Time (w/o XI) 12.093691 ‐0.10% ‐0.78% 0.10% ‐0.50% Transit Ridership 22,554 ‐0.20% ‐0.61% ‐0.59% 2.15%
% Mode Share DA (All) 49.3 ‐0.45% ‐0.53% ‐0.52% ‐1.29%
% Mode Share SR (All) 42.7 0.54% 0.60% 0.61% 1.34%
% Mode Share Transit (All) 1.1 ‐0.13% ‐0.30% ‐0.29% 2.47% % Mode Share Walk (All) 4.3 ‐0.21% 0.07% ‐0.12% 0.57%
% Mode Share Bike (All) 1.4 ‐0.34% ‐0.24% ‐0.32% 0.71%
% Mode Share School Bus (All) 1.2 0.39% 0.72% 0.69% 0.52% Auto Operating Cost ($) $2,005,004 ‐0.37% ‐1.72% ‐0.39% 92.87% ATTACHMENT 1
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The following are observations from the above sensitivity tests:
 Adding 1000 households in Santa Barbara City results in a smaller number of vehicle trips.

The share of auto is increased but shared ride trips are increased due to the proximity of the households to the HOV facilities on 101. In addition, households are closer to employment areas nearby and the thus the trips are slightly shorter.  Adding 1,000,000 low density commercial sqft in Lompoc reduces VMT by about 2%. This is due to more trips from households in Lompoc staying within Lompoc instead of going outside of the city.
 Adding 1000 employment to a zone in Santa Ynez causes a small (‐0.7%) reduction in VMT as it causes some trips to remain within Santa Ynez.
 A doubling of the auto operating costs, as a proxy to increased gas prices, results in a very small reduction of vehicle trips (‐0.2%), and a larger reduction in VMT (‐5.5%), as trips become shorter due to increased costs. There is also a small shift toward carpooling.

Table 64: Sensitivity Results Table 2
Performance Measure Base
Double
Value
of Time
Double
Frequency
Double
Jobs‐
Household
Diversity
Double
Parking
Costs
Double
Households
Increase
Arterial
Speeds
Vehicle Trips 1,383,520 ‐0.90% ‐0.87% ‐0.62% ‐0.60% 78.25% ‐0.36% Vehicle Miles (Total) 10,112,487 ‐3.55% ‐0.59% ‐0.69% ‐0.41% 55.34% 2.24% Vehicle Hours (Total) 213,669 0.32% ‐0.84% ‐0.78% ‐0.59% 76.52% ‐3.98% Vehicle Miles/Vehicle Trips 7.31 ‐2.67% 0.28% ‐0.07% 0.20% ‐12.86% 2.61% Vehicle Hours/Vehicle Trips 0.154 1.23% 0.03% ‐0.16% 0.01% ‐0.97% ‐3.63% Vehicle Miles/Capita 22.81 ‐3.55% ‐0.59% ‐0.69% ‐0.41% ‐22.33% 2.24% Vehicle Miles/Commercial KSF 210.19 ‐3.55% ‐0.59% ‐0.69% ‐0.41% 55.34% 2.24% Average Travel Distance (w/o XI) 6.12 ‐2.22% 0.36% ‐0.10% 0.34% ‐9.51% 4.29% Average Travel Time (w/o XI) 12.09 ‐0.50% 0.11% ‐0.04% 0.12% ‐0.18% ‐2.11% Transit Ridership 22,554 2.15% 28.20% ‐0.76% 0.21% 61.31% 1.01% % Mode Share DA (All) 49.3 ‐1.29% ‐0.92% ‐0.49% ‐0.62% 3.21% ‐0.39% % Mode Share SR (All) 42.7 1.34% 0.44% 0.58% 0.62% ‐1.96% 0.87% % Mode Share Transit (All) 1.1 2.47% 28.60% ‐0.35% 0.52% ‐7.80% 1.32% % Mode Share Walk (All) 4.3 0.57% ‐0.70% ‐0.14% 0.69% ‐1.84% ‐3.93% % Mode Share Bike (All) 1.4 0.71% ‐1.70% ‐0.28% ‐0.18% ‐5.79% ‐2.76% % Mode Share School Bus (All) 1.2 0.52% 0.66% 0.77% 0.70% ‐42.56% 1.08% Auto Operating Cost ($) $2,005,004 92.87% ‐0.59% ‐0.68% ‐0.40% 55.23% 2.28% The following are observations from the sensitivity tests from the table above:

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 Doubling the value of time increases VMT and trip distances slightly. An increase in value of time means that users care more about travel time than travel costs, so they are less sensitive to operating costs (i.e. gas prices). This causes trips to be longer distance‐wise if the user can save travel time.
 Doubling the frequency of transit routes causes over a 30% increase in transit ridership.

Most of that increases comes at the expense of walk and bike trips, but some come from auto trips, thus reducing total auto vehicle trips and VMT slightly.  Increasing 4Ds variable transit stop densities in downtown Lompoc results in a small shift (3.1%) to the transit mode and a small VMT reduction (‐1.1%).  The tripling of parking costs causes a small shift to transit, walk, and bike trips (2.8, 1.0, and

1.6% increases respectively) and a small decrease (‐0.2%) in drive alone trips. The result is a very small (‐0.1%) decrease in VMT.
 The increase of arterial speeds causes a 3.6% reduction in VHT, as vehicles can move faster along arterials and collectors. This causes a shift away from walk and bike modes, and an increase to drive and transit trips. Transit trips are increased because transit speeds are directly dependent upon auto speeds.
 A doubling of households in the region causes an almost 82% increase in vehicle trips, a 61% increase in VMT, and a 65.5% increase in VHT.

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Future Year Models
Several future year scenarios were developed: 2020, 2035, and 2050. Using the previous model’s UPLAN forecasts, estimates of households, employment, land use, and other demographic inputs into the model were derived for the forecast years. The TAZ zonal systems and growth forecasts for the updated model are slightly different from the previous model, so a specific procedure was performed to estimate the forecasted variables. The procedure is described below:

1. From the previous model’s future year forecasts, household, population, and employment growth rates were estimated at the TAZ level for each forecast interval (2010‐2020, 2020‐2035, and 2035‐2040)

2. SBCAG’s Regional Growth Forecast (RGF) estimates for employment, households, and population were obtained for each city and for each forecast year.

3. The growth rates from Step 1 were applied to the base year 2015 demographics to estimate TAZ‐based demographics for the future years. The demographics were normalized to match the RGF estimates.

Planned versions of each of the future years were developed for the highway, transit, walk, and bike networks. The following table describes the demographic summaries for each of the model years:

Table 65: Demographic Summaries for Future Years
Demographic Year
2015 2020 2035 2050
Population 443,312 460,800 501,500 521,600
Households 144,871 152,100 173,100 187,000
Agricultural Employment 5,594 5,345 5,060 4,779
Commercial Employment 75,957 76,264 84,312 90,080
Industrial Employment 23,135 26,188 30,874 35,634
Office Employment 24,620 28,498 38,110 41,876
Service Employment 84,915 86,545 92,024 98,230
Total Employment 213,700 222,840 250,380 270,600
Low Density Residential DU 112,555 112,397 116,519 118,479 High Density Residential DU 45,558 45,995 55,684 64,174 Low Density Commercial KSF 32,303 32,724 34,291 33,899
High Density Commercial KSF 15,831 16,294 16,841 17,864 Office KSF 12,302 12,568 12,834 12,952
Industry KSF 70,108 75,302 77,952 83,451
Institutional KSF 12,075 12,335 12,638 12,806
PreSchool Students 4,929 4,943 4,971 5,056
Elementary Enrollment 34,412 34,491 34,650 35,129
Middle School Enrollment 15,288 15,311 15,356 15,492
High School Enrollment 20,562 20,593 20,656 20,845
College Enrollment 49,811 53,235 60,575 85,745
ATTACHMENT 1
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Demographic Year
2015 2020 2035 2050
Private Elementary Enrollment 3,502 3,511 3,529 3,582
Private High Enrollment 1,656 1,659 1,664 1,681
Hotels and Motel Rooms 12,621 12,968 21,305 22,321
Parks and Open Space Acres 19,995 19,995 19,995 19,995
For the rest of the future year analysis, the future year 2050 is compared with base year 2015.

The future year 2050 scenario assumes Transit Oriented Demand (TOD) and infill strategies are utilitized.

Trip Generation
Balanced trips by trip purpose are compared between the base year 2015 and 2050. The results are listed in the following table:

Table 66: Trip Comparisons Between Base Year 2015 and 2050 Trip
Purpose
2015 Trips 2050 Trips Difference
HBW 346,232 409,610 18.31%
HBShop 137,099 161,997 18.16%
HBSchool 186,005 234,384 26.01%
HBO 636,582 748,906 17.64%
NHBW 205,214 243,504 18.66%
NHBO 276,481 327,904 18.60%
IXXI 205,917 284,469 38.15%
Visitor 71,406 82,893 16.09%
Total 2,064,935 2,493,668 20.76%
Overall, trips increased about 21%.

Trip Distribution
For trip distribution, average trip lengths by trip purpose and period comparisons are performed between the 2015 and 2050 scenarios:

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Table 67: Trip Length Comparisons Between Base Year 2015 and 2050 Average Distance Average Time
2015 2050 Difference 2015 2050 Difference
HBW PK 8.9 8.2 -8.42% 15.9 15.1 -4.94%
HBShop PK 4.3 4.1 -4.99% 10 9.9 -1.08%
HBO PK 4.5 4.4 -3.28% 10.5 10.4 -0.83%
NHBW PK 7.1 5.5 -22.28% 13.9 12.0 -13.83%
NHBO PK 6.6 3.9 -41.58% 12.3 9.6 -21.57%
IXXI PK 28.1 27.2 -3.17% 36.2 35.4 -2.16%
HBSch PK 6.5 8.9 36.18% 12.7 15.5 21.92%
HBW OP 9.7 8.7 -10.33% 16.1 15.1 -6.26%
HBShop OP 4.5 4.3 -4.90% 10.1 9.8 -2.54%
HBO OP 4.8 4.6 -4.81% 10.5 10.4 -1.42%
NHBW OP 5.5 4.7 -13.68% 11.4 10.7 -6.05%
NHBO OP 4.8 3.4 -28.84% 10.2 8.8 -13.96%
IXXI OP 25.4 25.1 -1.06% 32.3 32.5 0.54%
HBSch OP 5.9 8.7 48.08% 10.5 14.4 37.38%
For most purposes, trip lengths and travel times decreased. Even though trips increased, TOD and infill strategies brought productions and attractions closer together. School trip lengths increased because it is assumed that school locations remain unchanged over time regardless of development patterns. Infill development in the future moves housing and employment closer together, reducing trip lengths and travel times. For NHBW in particular, both production and attraction growth increased proportionally in the same cities Mode Choice
The table below compares the mode shares between the 2015 base year and the 2050 future year scenarios by trip purpose:

Table 68:Mode Share Comparisons Between Base Year 2015 and 2050 Trip
Purpose
Year Mode
DA SR Transit Walk Bike School Bus
Work 2015 85.00% 9.10% 0.63% 3.28% 2.05% 0.00%
2050 84.58% 9.02% 0.73% 3.54% 2.13% 0.00%
Difference -0.49% -0.88% 15.87% 7.93% 3.90% 0.00%
School 2015 17.03% 46.87% 2.17% 15.20% 5.63% 13.10%
2050 18.10% 47.45% 2.31% 13.14% 5.30% 13.70%
Difference 6.28% 1.24% 6.45% -13.55% -5.86% 4.58%
Other 2015 45.16% 49.80% 1.06% 3.27% 0.71% 0.00%
2050 45.20% 49.55% 1.15% 3.40% 0.69% 0.00%
Difference 0.09% -0.50% 8.49% 3.98% -2.82% 0.00%
Overall 2015 49.30% 42.71% 1.09% 4.34% 1.38% 1.18%
ATTACHMENT 1
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Trip
Purpose
Year Mode
DA SR Transit Walk Bike School Bus
2050 49.11% 42.71% 1.19% 4.34% 1.36% 1.29%
Difference -0.39% 0.00% 9.17% 0.00% -1.45% 9.32%

In general, drive alone and bike decreases slightly, while transit trips and school buses increased. The TOD improvements work to increase transit shares.

Time of Day
The table below compares drive‐alone and shared‐ride vehicle trips by time period for the base year 2010 and 2050 scenarios:

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Table 69: OD Vehicle Trips Between Base Year 2015 and Future Year 2050 Time
Period
DA Vehicle Trips SR2 Vehicle Trips Total
2015 2050 Difference 2015 2050 Difference 2015 2050 Difference AM 134,992 159,623 15.43% 57,252 69,342 17.44% 192,244 228,966 16.04% Late AM 178,335 214,994 17.05% 60,293 72,585 16.93% 238,628 287,578 17.02% Lunch 138,979 167,022 16.79% 43,441 52,115 16.64% 182,420 219,137 16.76% Early PM 155,011 187,634 17.39% 52,823 63,598 16.94% 207,834 251,231 17.27% PM 174,950 207,901 15.85% 74,126 89,478 17.16% 249,076 297,379 16.24% Evening 102,316 123,149 16.92% 34,911 41,789 16.46% 137,227 164,938 16.80% Late
Evening 63,939 77,087 17.06% 24,807 29,843 16.87% 88,746 106,929 17.00% Night 70,530 85,985 17.97% 16,815 20,622 18.46% 87,345 106,607 18.07% Total 1,019,052 1,223,395 16.70% 364,468 439,371 17.05% 1,383,520 1,662,766 16.79% In general, there is about a 17% increase in vehicle trips between the 2015 and 2050 scenarios.

Transit, walk, and bike trips were also compared between the 2015 and 2050 scenarios:

Table 70: Transit, Walk, and Bike Trips Between Base Year 2010 and Future Year 2040 Period Transit Trips
2015 2050 Difference
Peak 6,656 8,923 34.06%
Offpeak 15,898 20,843 31.10%
Total 22,554 29,766 31.98%
Mode Trips
2015 2050 Difference
Bike 28,453 33,983 19.44%
Walk 89,812 108,376 20.67%
Total 118,265 142,359 20.37%
In general, there is about a 32% increase in transit trips and a 20% increase in bike and walk trips in 2050 vs. 2015.

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Truck Trips
The following table compares truck trips between 2015 and 2050 by time period:

Time Period 2015 2050 Difference
AM (2 hr) 2,345 2,967 26.52%
Late AM (3 hr) 4,699 5,946 26.54%
Lunch (2 hr) 3,133 3,964 26.54%
Early PM (2 hr) 2,894 3,661 26.51%
PM (2 hr) 2,656 3,359 26.46%
Evening (2 hr) 1,801 2,276 26.35%
Late Evening (4 hr) 1,894 2,387 26.04%
Night (7 hr) 4,014 5,064 26.18%
Total 23,436 29,625 26.41%
In general, there is about a 26% increase in truck trips between the scenarios.

Assignment
The following map compares AM flows and congestion between 2015 and 2050 for the South Coast:

Figure 26: Base Year 2015 (Left) vs. Future Year 2050 (Right) AM Flow Map

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The following map compares PM flow and congestion between 2015 and 2050 for the South Coast:

Figure 27: : Base Year 2015 (Left) vs. Future Year 2050 (Right) PM Flow Map In general, the flow maps show more gradual congestion occurring in the South Coast in the future year 2050 scenario but a little less congestion on the 101 in the PM.

The following maps show flow comparisons for the AM and PM period in North Santa Barbara:

Figure 28: 2015 (Left) vs. 2050 (Right) AM Flow Comparisons for North Santa Barbara County Figure 29: 2015 (Left) vs. 2050(Right) PM Flow Comparisons for North Santa Barbara ATTACHMENT 1
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There is moderately more congestion in North Santa Barbara but the infill strategies lessen the congestion increases.

VMT and VHT by functional classification are compared between the two scenarios in the table below:

Table 71: VMT and VHT Comparisons between 2015 and 2050 Scenarios Functional
Class

VMT VHT
2015 2050 Difference 2015 2050 Difference
Freeways 4,902,820 5,404,013 10.22% 80,743 91,248 13.01% Expressways 0 0 0 0
Principal Arterial 1,105,560 1,275,038 15.33% 25,717 31,017 20.61% Minor Arterial 2,161,402 2,395,876 10.85% 50,230 56,027 11.54% Urban Collector 553,281 647,801 17.08% 13,353 15,993 19.77% Rural Collector 205,227 216,049 5.27% 5,755 6,060 5.30% Local 285,972 324,309 13.41% 10,242 12,855 25.52%
Centroid Connector 610,863 699,686 14.54% 20,362 23,323 14.54% Ramp 208,554 242,786 16.41% 6,585 7,859 19.35%
System to System
Ramp 25,917 27,362 5.57% 672 717 6.73%
HOV 636 252,351 39598.16% 10 4,001 39557.55%
Total 10,060,232 11,485,269 14.17% 213,669 249,102 16.58% The table below lists transit ridership results for 2050 and compares with 2015:

Table 72: Daily Transit Ridership Comparisons between 2015 and 2050 Company 2015 2050 Difference
Amtrak 12 15 22.42%
Breeze 639 787 23.13%
COLT 618 1,013 63.90%
Central Coast Shuttle 1,178 1,594 35.32%
Chumash Casino Resort 0 0
Clean Air Express 32 32 1.15%
Coastal Express Limited 18 21 15.28%
Greyhound 238 385 61.89%
MTD 13 33 153.76%
SLO RTA 116 149 28.24%
SMAT 51 56 10.18%
SMOOTH (inc. Cuyama, Los Alamos,
Guadalupe) 20,677 28,026 35.54%
SYVT 18 27 52.12%
Santa Barbara Airbus 4,930 6,125 24.24%
VISTA 603 653 8.36%
ATTACHMENT 1
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Company 2015 2050 Difference
WINE COUNTRY EXPRESS 0 61
Total 0 0 33.75%
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix
APPENDIX B – TRAFFIC ANALYSIS ZONES
ATTACHMENT 1
ATTACHMENT 1
ATTACHMENT 1
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-1
APPENDIX C – LAND USE AND SOCIOECONOMIC DATA (SED)
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-2
2020 Socioeconomic Data
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10002 101 42 42 0 0 0 0 0 0 0 0 42 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10003 441 184 184 0 0 0 0 0 0 0 0 184 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10004 367 158 5 153 0 15 0 0 1777 0 1793 26 132 0.91 20.35 0 0 0 0 0 0 0 291 0.00 0 0 10005 381 159 159 0 0 0 0 0 0 0 0 132 27 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10006 0 0 0 0 0 0 0 0 2434 0 2434 0 0 0.00 0.00 0 0 0 0 0 0 0 432 0.00 0 0 10007 22 9 9 0 0 0 0 0 0 0 0 6 3 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10008 331 139 111 28 0 0 0 0 0 0 0 139 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10009 1870 795 327 468 0 7 0 29 4 0 40 421 374 3.37 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10011 38 16 16 0 0 0 0 0 0 0 0 16 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10012 347 145 140 5 60 0 0 0 0 0 60 145 0 0.00 0.00 0 0 0 0 0 0 0 0 0.11 0 0 10013 582 250 26 224 0 7 6 0 0 0 14 60 190 1.44 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10014 622 266 65 201 0 62 97 5 1 0 165 158 108 12.41 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10015 222 94 56 38 0 0 0 0 0 1 1 94 0 0.00 0.00 10 0 0 0 0 0 0 0 0.00 0 0 10016 0 0 0 0 0 14 0 1 59 0 74 0 0 3.08 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10017 801 334 334 0 0 0 0 0 1 0 1 291 43 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10018 1304 545 508 37 0 0 0 0 0 0 0 449 96 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10019 2028 866 237 629 0 125 0 12 115 13 266 460 406 27.72 0.00 19 0 0 0 0 174 79 0 0.00 0 0 10020 1395 586 460 126 0 0 0 0 0 32 32 586 0 0.00 0.00 0 268 460 0 0 0 0 0 0.00 0 0 10021 111 48 1 47 0 8 0 0 293 0 301 6 42 0.00 5.09 0 0 0 0 11862 0 0 49 0.00 0 0 10022 1780 751 489 262 0 70 0 10 131 0 211 616 135 20.85 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10023 1195 502 392 110 0 21 0 0 0 0 21 425 77 4.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10024 0 0 0 0 0 53 0 1 155 0 209 0 0 0.00 24.43 0 0 0 0 0 0 0 0 0.00 0 0 10025 793 340 51 289 0 0 0 0 0 0 0 259 81 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10026 0 0 0 0 0 0 130 0 0 0 130 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10027 0 0 0 0 0 0 0 0 0 23 23 0 0 0.00 0.00 0 482 74 0 0 0 0 0 0.00 0 0 10028 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10029 534 223 212 11 0 5 0 0 15 0 20 223 0 1.14 1.86 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-3
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10033 79 33 27 6 0 102 0 47 193 0 342 33 0 22.61 13.93 0 0 0 0 0 0 7 0 0.00 0 0 10034 225 94 94 0 0 0 0 0 0 0 0 94 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10035 5 2 2 0 0 0 0 0 0 0 0 1 1 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10036 946 403 135 268 0 0 0 67 0 0 67 403 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10037 530 228 11 217 0 0 0 0 0 0 0 60 168 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10038 384 160 160 0 0 0 0 0 0 21 21 160 0 0.00 0.00 0 419 68 0 0 0 0 0 0.00 0 0 10039 103 43 43 0 0 0 0 0 0 0 0 43 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10040 458 191 191 0 0 0 0 0 0 0 0 187 4 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10041 3768 1574 1504 70 0 0 0 0 0 0 0 1031 543 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10042 355 148 146 2 84 0 0 0 0 0 84 125 23 0.00 0.00 0 0 0 0 0 0 0 0 0.15 0 0 10043 883 371 289 82 0 21 0 9 283 19 331 230 141 6.02 2.10 188 0 0 0 0 0 0 0 0.00 0 0 10045 283 118 118 0 0 0 0 0 0 0 0 118 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10046 610 263 0 263 0 2591 0 16 140 0 2748 263 0 92.67 455.94 0 0 0 0 0 0 0 0 0.00 0 0 10048 306 128 114 14 0 0 0 0 0 101 101 128 0 0.00 0.00 13 0 0 2187 0 0 0 0 0.00 0 0 10049 84 36 0 36 0 1017 0 30 6 0 1053 22 14 193.95 7.00 0 0 0 0 0 0 0 0 0.00 0 0 10052 763 328 30 298 0 759 0 0 341 0 1100 94 234 128.33 29.86 0 0 0 0 0 0 0 52 0.00 0 0 10053 680 290 89 201 0 340 0 126 357 0 823 290 0 127.98 4.74 0 0 0 0 0 0 0 0 0.00 0 0 10054 12 5 5 0 0 94 0 0 376 0 470 5 0 14.29 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10055 350 148 80 68 0 0 0 14 0 0 14 74 74 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10056 181 76 59 17 0 0 0 0 0 0 0 55 21 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10057 152 64 39 25 46 24 0 0 2 0 71 31 33 5.52 0.00 0 0 0 0 0 0 0 0 0.08 0 0 10058 34 14 14 0 0 0 0 0 0 14 14 14 0 0.00 0.00 0 286 54 0 0 0 0 0 0.00 0 0 10060 1609 671 671 0 0 0 0 0 0 0 0 547 124 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10061 381 159 159 0 0 0 0 0 0 0 0 132 27 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10064 235 98 96 2 0 2 0 0 20 0 22 91 7 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10065 963 402 392 10 0 0 0 0 0 25 25 348 54 0.00 0.00 60 383 73 0 0 0 0 0 0.00 0 0 10066 237 99 99 0 0 0 0 0 0 0 0 71 28 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10067 348 145 145 0 0 0 0 0 0 0 0 145 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10071 396 167 112 55 0 0 0 0 0 0 0 69 98 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-4
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10073 101 42 42 0 0 106 0 0 0 0 106 36 6 7.79 12.72 0 0 0 0 0 0 0 0 0.00 0 0 10074 295 123 123 0 0 10 36 0 0 0 46 74 49 2.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10075 127 53 53 0 0 0 0 0 0 0 0 52 1 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10076 298 125 101 24 0 4 37 0 34 0 76 52 73 5.34 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10077 240 100 100 0 0 0 0 0 0 0 0 100 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10078 0 0 0 0 0 0 0 30 33 0 63 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10079 38 16 16 0 0 0 0 0 0 0 0 12 4 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10080 0 0 0 0 0 144 0 92 86 0 323 0 0 61.67 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10081 232 98 56 42 0 0 0 0 0 0 0 62 36 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10082 350 151 0 151 0 599 0 32 28 0 659 75 76 111.85 16.19 0 0 0 0 0 0 0 0 0.00 0 0 10083 1075 453 313 140 0 0 0 0 68 0 68 243 210 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10084 471 201 54 147 0 7 0 0 107 31 144 93 108 0.00 0.00 0 367 88 0 0 0 256 0 0.00 0 0 10085 626 266 119 147 0 0 0 0 0 0 0 133 133 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10086 996 422 223 199 0 0 0 30 37 15 81 306 116 0.00 0.00 0 294 50 0 0 0 0 0 0.00 0 0 10087 643 276 27 249 0 0 0 0 0 0 0 148 128 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10088 265 114 1 113 0 0 0 333 0 0 333 13 101 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10089 685 292 96 196 0 0 56 0 0 0 56 153 139 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10090 299 129 0 129 0 270 0 172 39 0 481 3 126 63.19 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10091 1179 493 458 35 0 0 0 0 0 0 0 355 138 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10092 500 215 14 201 0 30 0 223 117 0 370 95 120 1.98 12.84 0 0 0 0 0 0 0 0 0.00 0 0 10093 0 0 0 0 0 15 0 10 0 0 25 0 0 2.92 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10096 350 151 0 151 0 37 0 75 9 0 122 0 151 0.00 12.80 0 0 0 0 0 0 0 0 0.00 0 0 10097 70 30 0 30 0 55 0 167 235 0 457 0 30 2.36 22.25 0 0 0 0 0 0 0 31 0.00 0 0 10102 692 295 98 197 0 0 0 0 0 0 0 158 137 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10103 834 348 341 7 0 0 0 0 26 6 32 348 0 0.00 0.00 62 0 0 0 0 0 0 0 0.00 0 0 10105 65 27 26 1 0 0 0 0 0 0 0 21 6 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10106 526 223 111 112 0 0 0 0 3 0 3 218 5 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10108 243 102 85 17 0 0 0 0 0 0 0 88 14 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10109 110 47 14 33 0 10 0 11 25 36 81 42 5 3.44 0.00 32 687 94 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-5
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10110 0 0 0 0 0 168 0 0 0 0 168 0 0 0.00 32.64 0 0 0 0 0 0 0 0 0.00 0 0 10111 174 75 0 75 0 304 0 7 41 0 351 0 75 64.84 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10113 202 87 6 81 0 104 0 105 44 0 253 36 51 30.52 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10114 494 206 206 0 0 0 0 0 0 0 0 141 65 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10115 1079 450 450 0 0 0 0 0 0 0 0 450 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10116 657 275 247 28 0 0 0 0 279 0 279 271 4 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10117 2039 875 114 761 0 16 0 0 6 21 43 155 720 1.49 2.43 26 364 60 0 0 0 0 0 0.00 0 0 10118 939 392 378 14 0 0 0 0 706 0 706 392 0 0.00 0.00 0 0 0 0 0 0 0 120 0.00 0 0 10119 449 190 105 85 0 0 0 0 0 0 0 114 76 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10120 675 287 113 174 0 0 0 0 0 53 53 114 173 0.00 0.00 0 1088 167 0 0 0 0 0 0.00 0 0 10121 263 111 77 34 0 82 0 0 701 6 789 111 0 6.36 0.00 0 0 0 0 0 85 44 0 0.00 0 0 10122 157 67 14 53 0 41 0 221 0 0 262 67 0 8.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10123 1241 524 322 202 0 0 0 69 22 0 91 494 30 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10124 796 339 123 216 0 0 0 0 4 99 103 118 221 0.00 0.00 0 0 0 2188 0 0 0 0 0.00 0 0 10125 0 0 0 0 0 431 0 172 239 0 842 0 0 98.06 46.52 0 0 0 0 0 0 0 0 0.00 0 0 10126 316 136 0 136 0 409 0 31 297 0 736 21 115 44.71 38.05 0 0 0 0 0 0 0 47 0.00 0 0 10127 313 134 31 103 0 0 0 0 7 0 7 38 96 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10128 0 0 0 0 0 379 0 154 26 0 559 0 0 50.40 30.10 0 0 0 0 0 0 0 0 0.00 0 0 10129 134 57 28 29 0 215 17 74 36 0 342 22 35 22.96 24.74 0 0 0 0 0 0 0 0 0.00 0 0 10130 244 105 3 102 0 99 4 6 21 0 130 39 66 24.46 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10131 23 10 0 10 0 86 0 228 3 0 317 0 10 17.85 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10132 97 41 19 22 0 16 0 0 4 0 21 20 21 2.13 3.47 0 0 0 0 0 0 0 0 0.00 0 0 10134 186 80 11 69 0 0 0 0 0 0 0 20 60 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10135 202 87 7 80 0 0 7 0 0 7 14 5 82 0.00 0.00 0 0 0 154 0 0 0 0 0.00 0 0 10136 163 70 5 65 0 0 0 0 5 0 5 16 54 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10137 119 51 5 46 0 24 18 41 21 0 104 5 46 7.48 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10138 0 0 0 0 0 0 0 369 0 0 369 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10139 103 44 9 35 0 18 0 139 261 0 419 14 30 6.24 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10140 5 2 1 1 0 102 8 106 22 0 238 2 0 13.58 19.03 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-6
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10141 2 1 0 1 0 126 0 1 67 0 195 1 0 25.56 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10144 541 227 183 44 0 0 0 0 277 0 277 151 76 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10145 357 153 30 123 0 1 0 0 27 0 28 56 97 1.37 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10147 579 242 229 13 0 0 0 0 0 16 16 242 0 0.00 0.00 0 308 64 0 0 0 0 0 0.00 0 0 10148 628 270 19 251 0 0 0 137 0 0 137 43 227 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10150 128 55 2 53 0 0 0 56 33 0 89 0 55 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10151 826 345 335 10 0 0 0 0 0 0 0 345 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10152 297 124 124 0 0 0 0 0 0 15 15 124 0 0.00 0.00 57 0 0 0 0 224 0 0 0.00 0 0 10153 0 0 0 0 0 1800 0 250 22 0 2072 0 0 76.69 276.76 0 0 0 0 0 0 0 0 0.00 0 0 10154 0 0 0 0 0 460 0 106 119 0 685 0 0 79.17 14.67 0 0 0 0 0 0 0 0 0.00 0 0 10155 171 73 15 58 0 0 0 0 0 0 0 11 62 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10156 340 146 14 132 0 0 0 0 48 0 48 24 122 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10157 624 266 87 179 0 0 0 0 85 0 85 96 170 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10158 9 4 0 4 0 200 0 35 1048 0 1284 2 2 17.54 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10159 393 169 8 161 0 61 0 11 42 0 114 2 167 1.88 9.76 0 0 0 0 0 0 0 0 0.00 0 0 10160 161 69 8 61 0 43 2 10 34 0 89 19 50 6.62 3.05 0 0 0 0 0 0 0 0 0.00 0 0 10161 172 73 35 38 0 27 0 73 48 0 149 42 31 6.38 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10162 570 245 23 222 0 0 0 0 0 0 0 62 183 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10163 360 155 9 146 0 15 0 50 34 2 100 15 140 7.36 0.00 15 0 0 0 0 0 0 0 0.00 0 0 10164 256 110 8 102 0 0 0 0 0 0 0 11 99 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10165 88 38 2 36 0 0 0 0 0 0 0 1 37 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10166 254 109 13 96 0 54 0 46 8 0 107 28 81 11.50 0.00 0 0 0 0 993 0 0 0 0.00 0 0 10167 762 325 104 221 0 80 0 0 37 3 119 118 207 19.33 1.03 25 0 0 0 0 0 0 0 0.00 0 0 10168 1364 584 116 468 0 5 0 12 22 6 44 166 418 4.44 0.00 60 0 0 0 0 0 0 0 0.00 0 0 10169 364 152 142 10 0 0 0 0 0 0 0 104 48 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10170 498 209 171 38 94 0 0 0 0 0 94 209 0 0.00 0.00 0 0 0 0 0 0 0 0 0.17 0 0 10171 325 136 116 20 0 0 0 0 0 0 0 112 24 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10172 48 20 18 2 0 0 0 0 0 0 0 16 4 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10173 144 60 60 0 0 0 0 0 0 3 3 60 0 0.00 0.00 34 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-7
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10174 177 75 42 33 0 1 0 0 1 0 2 75 0 0.19 0.31 0 0 0 0 0 0 0 0 0.00 0 0 10175 0 0 0 0 0 277 0 42 81 0 400 0 0 64.93 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10176 0 0 0 0 0 281 0 100 57 0 438 0 0 60.20 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10177 261 110 73 37 0 0 0 0 0 0 0 81 29 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10178 486 206 99 107 0 74 0 0 0 0 74 168 38 14.43 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10179 177 76 7 69 0 0 0 0 7 0 7 15 61 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10180 288 124 8 116 0 4 0 0 0 0 4 10 114 0.77 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10181 0 0 0 0 0 32 0 132 20 0 183 0 0 5.73 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10182 207 89 0 89 0 30 0 17 44 0 91 2 87 1.16 5.48 0 0 0 0 0 0 0 0 0.00 0 0 10183 101 43 12 31 0 0 0 0 11 3 13 23 20 0.00 0.00 26 0 0 0 0 0 0 0 0.00 0 0 10184 536 224 216 8 0 0 0 0 0 0 0 224 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10185 245 103 77 26 0 0 0 0 0 18 18 103 0 0.00 0.00 0 357 64 0 0 0 0 0 0.00 0 0 10186 550 231 179 52 0 0 0 0 0 0 0 201 30 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10187 631 269 91 178 0 0 0 0 0 0 0 81 188 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10188 54 23 4 19 0 25 0 113 229 8 375 1 22 0.00 0.00 85 0 0 0 0 0 0 0 0.00 0 0 10189 574 246 40 206 0 95 5 20 11 0 132 41 205 13.49 11.33 0 0 0 0 0 0 0 0 0.00 0 0 10190 174 75 0 75 0 6 0 268 2 0 275 2 73 2.07 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10193 321 138 10 128 0 0 0 196 24 0 220 15 123 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10194 0 0 0 0 0 138 0 0 3688 0 3826 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10195 812 343 213 130 0 10 0 0 3 0 13 343 0 2.36 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10196 14 6 1 5 0 5 0 59 209 0 272 0 6 0.37 1.22 0 0 0 0 0 0 0 37 0.00 0 0 10199 567 244 17 227 0 3 0 0 2 2 7 34 210 1.61 0.00 19 0 0 0 0 0 0 0 0.00 0 0 10200 12 5 1 4 0 1 0 214 0 0 215 3 2 0.37 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10201 84 36 9 27 0 0 0 49 0 0 49 7 29 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10202 216 93 6 87 0 0 0 11 0 0 11 18 75 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10203 2 1 1 0 0 0 0 39 2279 0 2317 1 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10204 414 176 79 97 0 0 0 0 11 0 11 88 88 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10205 337 145 3 142 0 110 0 40 46 0 196 145 0 28.09 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10206 178 76 20 56 0 0 0 0 0 0 0 57 19 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-8
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10208 258 111 8 103 0 0 0 0 0 0 0 20 91 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10209 1404 604 36 568 0 43 0 14 16 0 73 68 536 11.60 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10211 246 105 26 79 0 11 0 7 12 0 30 42 63 5.71 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10212 282 121 20 101 0 0 0 0 0 0 0 27 94 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10213 116 50 5 45 0 0 0 5 24 0 29 9 41 0.04 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10214 266 114 25 89 0 0 0 0 0 0 0 52 62 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10215 226 97 14 83 0 0 0 0 25 0 25 19 78 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10216 271 116 27 89 0 0 0 0 0 0 0 18 98 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10217 477 205 17 188 0 2 0 0 1 0 3 20 185 0.39 0.64 0 0 0 0 0 0 0 0 0.00 0 0 10218 9 4 0 4 0 110 17 142 324 0 594 2 2 16.69 17.25 0 0 0 0 0 0 0 0 0.00 0 0 10219 353 152 1 151 0 64 0 146 1 0 211 2 150 12.64 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10221 592 255 4 251 0 51 0 4 3 0 57 15 240 11.10 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10222 88 38 1 37 0 164 7 74 13 0 258 26 12 33.84 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10223 237 102 9 93 0 21 0 14 2 0 37 5 97 4.87 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10224 152 65 20 45 0 0 0 0 0 7 7 11 54 0.00 0.00 66 0 0 0 0 0 0 0 0.00 0 0 10225 102 44 1 43 0 180 3 20 27 0 230 3 41 38.68 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10226 193 83 10 73 0 71 0 9 10 0 90 10 73 16.38 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10228 237 102 3 99 0 23 0 45 0 0 67 8 94 4.38 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10229 191 82 5 77 0 0 0 0 22 0 22 16 66 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10230 135 58 10 48 0 16 7 53 16 0 91 14 44 6.19 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10231 0 0 0 0 0 277 0 54 9 0 340 0 0 9.89 48.19 0 0 0 0 0 0 0 0 0.00 0 0 10232 0 0 0 0 0 404 0 76 48 0 529 0 0 81.82 6.02 0 0 0 0 0 0 0 0 0.00 0 0 10234 61 26 11 15 0 0 0 0 0 0 0 20 6 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10235 89 38 9 29 0 0 0 189 15 0 204 11 27 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10236 171 73 18 55 0 0 0 0 0 0 0 14 59 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10237 238 102 11 91 0 0 0 0 0 0 0 5 97 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10238 48 20 20 0 0 0 0 0 0 0 0 20 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10239 48 20 20 0 0 0 0 0 0 0 0 20 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10240 139 59 28 31 0 0 0 22 0 4 26 59 0 0.00 0.00 39 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-9
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10241 135 58 10 48 0 5 0 96 0 0 101 47 11 0.00 0.99 0 0 0 0 0 0 0 0 0.00 0 0 10242 35 15 8 7 0 22 0 46 129 0 197 4 11 0.00 5.88 0 0 0 0 0 0 0 22 0.00 0 0 10243 75 32 14 18 0 0 0 0 0 0 0 23 9 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10244 54 23 2 21 0 27 0 204 42 0 272 12 11 3.04 7.72 0 0 0 0 0 0 0 0 0.00 0 0 10245 40 17 4 13 0 44 0 101 121 0 267 0 17 0.98 9.10 0 0 0 0 0 0 0 0 0.00 0 0 10246 77 33 6 27 0 15 0 45 396 0 455 3 30 0.36 1.43 0 0 0 0 0 0 0 61 0.00 0 0 10247 255 110 3 107 0 0 0 0 0 0 0 66 44 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10248 149 64 4 60 0 27 0 43 49 3 122 26 38 2.41 2.49 26 0 0 0 0 0 0 0 0.00 0 0 10249 86 37 4 33 0 3 0 0 66 0 69 7 30 1.71 2.79 0 0 0 0 0 0 0 0 0.00 0 0 10250 45 19 15 4 0 0 0 0 0 0 0 16 3 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10251 46 19 19 0 0 0 0 0 0 0 0 19 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10252 38 16 16 0 0 0 0 0 0 0 0 16 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10253 102 44 2 42 0 46 11 119 4 0 180 2 42 10.91 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10254 188 81 6 75 0 0 0 0 0 0 0 8 73 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10255 240 103 8 95 0 0 0 0 0 0 0 15 88 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10256 226 97 9 88 0 0 0 0 0 0 0 26 71 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10257 163 70 10 60 0 0 0 0 13 0 13 12 58 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10258 194 83 14 69 0 0 0 18 20 0 38 26 57 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10259 147 63 6 57 0 19 0 76 0 0 95 9 54 3.70 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10260 177 76 9 67 0 0 0 0 0 0 0 7 69 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10261 202 87 8 79 0 0 0 0 4 0 4 13 74 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10262 187 80 12 68 0 0 0 0 0 0 0 27 53 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10263 170 73 13 60 0 0 0 21 0 0 21 21 52 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10264 47 20 2 18 0 122 24 10 27 0 183 3 17 25.42 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10265 239 103 5 98 0 0 0 0 0 0 0 7 96 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10266 114 49 0 49 0 45 0 54 59 0 158 15 34 8.29 5.30 0 0 0 0 0 0 0 0 0.00 0 0 10267 43 18 14 4 0 14 0 20 49 0 84 18 0 0.96 3.74 0 0 0 0 0 0 0 0 0.00 0 0 10268 78 33 18 15 0 0 0 0 74 0 74 25 8 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10269 79 33 29 4 0 0 0 0 0 0 0 32 1 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-10
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10270 52 22 18 4 0 0 0 0 0 0 0 22 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10271 58 24 24 0 0 0 0 0 114 0 114 19 5 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10272 31 13 13 0 0 0 0 0 0 0 0 11 2 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10273 132 57 2 55 0 140 0 43 34 0 217 18 39 26.77 10.17 0 0 0 0 0 0 0 0 0.00 0 0 10274 32 14 0 14 0 469 0 33 123 0 625 0 14 44.63 64.45 0 0 0 0 76 0 0 0 0.00 0 0 10275 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10276 9 4 0 4 0 12 0 34 55 7 108 3 1 2.24 0.00 0 0 0 0 0 170 0 0 0.00 0 0 10277 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10278 0 0 0 0 0 0 0 0 0 1 1 0 0 0.00 0.00 13 0 0 0 0 0 0 0 0.00 0 0 10279 0 0 0 0 0 18 0 37 173 0 227 0 0 1.15 2.27 0 0 0 0 0 0 0 0 0.00 0 0 10280 123 53 1 52 0 2 0 164 17 0 183 12 41 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10281 149 64 1 63 0 52 0 58 0 0 110 2 62 10.06 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10282 5 2 0 2 0 115 0 167 25 0 307 0 2 16.79 15.73 0 0 0 0 0 0 0 0 0.00 0 0 10283 165 71 0 71 0 865 0 20 59 0 945 42 29 93.52 81.98 0 0 0 0 0 0 0 0 0.00 0 0 10284 107 46 4 42 0 0 0 85 59 1 146 9 37 0.00 0.00 0 0 0 0 0 30 0 0 0.00 0 0 10285 19 8 2 6 0 0 0 34 25 0 59 1 7 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10286 2 1 0 1 0 30 0 76 17 0 124 0 1 8.25 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10287 351 151 4 147 0 0 0 0 0 0 0 6 145 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10288 509 217 77 140 0 0 0 0 0 0 0 73 144 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10289 96 41 16 25 0 0 0 0 0 0 0 31 10 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10290 62 26 16 10 0 0 0 0 0 0 0 16 10 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10292 655 275 217 58 0 0 0 0 0 0 0 192 83 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10293 806 336 336 0 0 0 0 0 59 35 94 336 0 0.00 0.00 0 643 109 0 0 83 0 0 0.00 0 0 10294 606 253 243 10 0 42 0 11 14 0 67 253 0 10.02 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10295 7 3 0 3 0 115 0 30 65 0 210 3 0 34.26 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10296 0 0 0 0 0 158 0 6 54 0 218 0 0 29.27 3.46 0 0 0 0 0 0 0 0 0.00 0 0 10297 301 129 16 113 0 7 0 10 0 0 17 30 99 0.51 0.84 0 0 0 0 0 0 0 0 0.00 0 0 10298 7 3 0 3 0 150 120 0 69 0 339 3 0 49.09 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10299 52 22 9 13 0 47 274 28 48 0 397 7 15 14.16 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-11
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10300 0 0 0 0 0 200 915 101 163 0 1380 0 0 20.87 20.87 0 0 0 0 0 0 0 0 0.00 0 0 10301 2 1 1 0 0 0 0 79 0 5 84 0 1 0.00 0.00 0 0 0 113 0 0 0 0 0.00 0 0 10302 10 4 4 0 0 48 166 11 130 0 355 4 0 8.08 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10303 149 64 1 63 0 28 1 92 19 0 139 14 50 14.27 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10304 2 1 1 0 0 8 0 181 88 0 277 1 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10305 26 11 1 10 0 2 0 293 88 0 382 1 10 0.00 2.20 0 0 0 0 0 0 0 0 0.00 0 0 10306 390 165 92 73 0 96 29 8 73 0 205 135 30 31.50 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10307 63 27 5 22 0 98 0 10 26 0 134 5 22 22.46 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10308 200 85 33 52 0 0 0 0 0 0 0 29 56 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10309 31 13 6 7 0 98 62 128 46 0 334 0 13 29.40 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10310 28 12 1 11 0 125 80 77 22 0 304 3 9 28.94 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10311 237 102 0 102 0 0 0 0 0 0 0 102 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10312 0 0 0 0 0 0 82 339 0 0 421 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10313 0 0 0 0 0 21 17 0 193 0 232 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10314 451 193 45 148 0 0 0 0 11 0 11 75 118 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10315 704 302 37 265 0 15 1 0 4 0 20 182 120 3.40 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10316 0 0 0 0 0 0 0 49 0 0 49 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10317 30 13 2 11 0 3 0 122 8 2 135 5 8 2.02 0.00 0 0 0 0 0 18 26 0 0.00 0 0 10319 2 1 0 1 0 241 104 99 95 0 539 1 0 60.76 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10320 99 42 15 27 0 77 111 20 56 0 265 33 9 20.78 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10321 106 45 22 23 0 34 0 0 0 0 34 38 7 6.57 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10322 12 5 5 0 0 69 0 8 48 0 125 4 1 19.87 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10323 0 0 0 0 0 2 62 132 19 0 215 0 0 1.45 1.45 0 0 0 0 0 0 0 0 0.00 0 0 10324 28 12 5 7 0 0 0 0 0 0 0 2 10 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10325 632 266 188 78 0 0 0 0 0 0 0 203 63 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10326 362 154 58 96 0 0 0 0 0 0 0 106 48 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10327 0 0 0 0 11 136 257 2 30 0 435 0 0 34.94 0.00 0 0 0 0 0 0 0 0 0.02 0 0 10328 0 0 0 0 0 39 0 0 15 0 53 0 0 7.53 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10329 53 23 1 22 0 11 21 70 98 0 201 1 22 0.47 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-12
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10330 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10331 33 14 5 9 0 90 43 26 8 0 168 7 7 21.34 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10332 0 0 0 0 0 111 45 48 41 0 244 0 0 27.46 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10333 634 273 8 265 0 66 0 0 596 0 662 13 260 1.22 24.27 0 0 0 0 0 0 0 98 0.00 0 0 10334 21 9 1 8 0 306 0 0 42 0 348 0 9 57.75 6.15 0 0 0 0 0 0 0 0 0.00 0 0 10335 230 99 10 89 0 37 0 0 5 0 43 15 84 7.25 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10336 72 31 0 31 0 213 0 17 22 0 252 24 7 44.41 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10337 84 36 4 32 0 82 15 97 5 0 199 13 23 17.03 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10338 202 87 5 82 0 8 0 23 103 0 134 14 73 4.06 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10339 126 54 10 44 0 12 7 33 1 0 53 6 48 2.83 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10340 228 98 3 95 0 0 0 0 0 0 0 3 95 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10341 45 19 6 13 0 83 32 0 71 4 189 6 13 25.46 0.00 39 0 0 0 0 0 0 0 0.00 0 0 10342 84 36 1 35 0 177 30 0 21 0 229 0 36 35.95 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10343 47 20 8 12 0 64 54 136 53 0 307 9 11 24.23 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10344 19 8 5 3 0 256 106 28 170 0 560 8 0 55.67 13.12 0 0 0 0 0 0 0 0 0.00 0 0 10345 0 0 0 0 0 60 0 0 163 0 223 0 0 13.05 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10346 30 13 2 11 0 980 268 113 8 0 1368 0 13 192.15 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10347 184 79 3 76 0 88 21 21 20 0 150 21 58 18.77 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10348 9 4 0 4 0 143 0 13 8 0 164 1 3 31.43 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10349 65 28 0 28 0 3 0 0 823 0 825 25 3 0.19 3.63 0 0 0 0 0 0 0 142 0.00 0 0 10350 269 116 3 113 0 139 31 6 137 0 312 12 104 43.59 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10351 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10353 14 6 0 6 0 64 6 0 94 0 164 2 4 8.80 2.55 0 0 0 0 0 0 0 0 0.00 0 0 10355 198 85 7 78 0 3 0 0 45 0 49 33 52 1.12 1.47 0 0 0 0 0 0 0 0 0.00 0 0 10356 218 94 2 92 0 15 0 2 129 0 147 12 82 2.12 6.16 0 0 0 0 0 0 0 0 0.00 0 0 10357 52 22 6 16 0 132 0 76 0 0 208 5 17 25.60 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10358 0 0 0 0 0 676 0 276 230 0 1182 0 0 129.85 27.86 0 0 0 0 0 0 0 0 0.00 0 0 10359 0 0 0 0 0 222 0 45 411 0 678 0 0 69.90 0.00 0 0 0 0 120 0 0 30 0.00 0 0 10360 105 45 10 35 0 61 0 22 4 0 88 28 17 13.91 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-13
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10361 12 5 2 3 0 51 0 57 195 0 304 0 5 8.42 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10362 42 18 0 18 0 168 0 85 71 0 325 18 0 54.85 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10363 0 0 0 0 0 398 10 0 28 0 436 0 0 61.24 19.90 0 0 0 0 0 0 0 0 0.00 0 0 10364 53 23 0 23 0 212 0 49 286 0 547 0 23 49.08 0.00 0 0 0 0 0 0 0 38 0.00 0 0 10365 0 0 0 0 0 302 12 26 19 0 360 0 0 67.73 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10366 72 31 5 26 0 100 0 40 38 0 178 3 28 23.95 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10367 9 4 0 4 0 205 0 70 314 0 589 1 3 41.91 15.27 0 0 0 0 0 0 0 30 0.00 0 0 10368 102 44 2 42 0 82 28 90 23 0 223 41 3 15.40 0.00 0 0 0 0 361 0 0 0 0.00 0 0 10369 91 39 6 33 0 56 0 16 1008 5 1085 22 17 5.87 14.12 46 0 0 0 0 0 0 145 0.00 0 0 10370 0 0 0 0 0 319 49 0 53 0 421 0 0 13.98 55.24 0 0 0 0 0 0 0 0 0.00 0 0 10371 0 0 0 0 0 303 9 0 8 0 320 0 0 62.81 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10373 42 18 4 14 0 38 85 11 18 0 152 12 6 10.50 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10374 0 0 0 0 0 56 36 26 40 0 157 0 0 11.33 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10375 100 43 0 43 0 31 0 0 0 0 31 43 0 6.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10377 24 10 4 6 0 136 77 5 38 0 255 3 7 36.73 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10378 19 8 0 8 0 128 93 82 15 0 318 4 4 30.91 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10380 111 48 0 48 0 68 0 37 15 0 121 28 20 22.38 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10381 2 1 1 0 0 0 0 0 0 0 0 0 1 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10382 112 48 4 44 0 0 0 0 0 0 0 6 42 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10383 616 258 221 37 0 5 0 0 9 0 13 258 0 2.09 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10384 499 214 34 180 0 44 54 10 10 0 118 53 161 13.04 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10386 614 261 109 152 0 2 0 4 25 7 38 133 128 0.00 0.00 0 0 0 0 0 130 37 0 0.00 0 0 10387 166 71 11 60 0 0 0 0 0 0 0 17 54 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10388 165 70 27 43 0 62 0 6 5 0 73 45 25 14.71 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10389 200 86 3 83 0 0 0 15 0 0 15 3 83 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10390 91 39 5 34 0 0 0 0 7 0 7 12 27 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10391 225 97 4 93 0 3 0 97 7 0 107 23 74 0.50 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10392 435 187 18 169 0 0 0 0 0 0 0 29 158 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10393 278 119 19 100 0 0 0 0 0 0 0 86 33 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-14
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10394 189 81 10 71 0 0 0 39 0 0 39 6 75 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10395 101 43 18 25 0 0 0 221 0 1 222 9 34 0.00 0.00 13 0 0 0 0 0 0 0 0.00 0 0 10396 79 34 1 33 0 10 10 120 0 0 140 11 23 1.99 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10397 5 2 2 0 0 7 0 209 2 0 218 2 0 2.48 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10398 167 72 4 68 0 0 0 68 0 0 68 14 58 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10399 144 62 1 61 0 23 0 16 0 0 39 11 51 4.53 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10400 12 5 3 2 0 135 192 0 3 34 364 3 2 27.61 0.00 0 0 747 0 0 0 0 0 0.00 0 0 10401 179 77 1 76 0 40 6 6 52 0 104 19 58 9.12 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10402 59 25 10 15 0 74 50 10 45 0 179 21 4 25.37 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10403 195 84 2 82 0 81 0 13 49 0 143 0 84 29.69 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10405 131 56 12 44 0 0 0 0 0 0 0 15 41 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10406 40 17 3 14 0 124 141 13 15 0 293 2 15 26.37 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10407 38 16 8 8 0 100 98 3 63 0 263 11 5 41.05 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10408 59 25 12 13 0 51 79 31 3 0 165 20 5 11.69 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10409 35 15 5 10 0 238 0 0 0 0 238 4 11 46.41 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10410 12 5 1 4 0 211 312 8 279 0 811 0 5 66.18 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10412 136 58 17 41 0 79 0 0 0 0 79 15 43 5.88 9.60 0 0 0 0 0 0 0 0 0.00 0 0 10414 38 16 10 6 0 32 91 14 0 0 137 5 11 6.23 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10415 19 8 0 8 0 50 88 12 25 0 175 0 8 13.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10416 0 0 0 0 0 39 0 3 10 0 52 0 0 12.94 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10419 235 101 14 87 0 0 0 0 0 0 0 11 90 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10420 577 245 110 135 0 0 0 0 0 0 0 131 114 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10421 475 202 85 117 0 13 0 0 114 49 175 77 125 0.00 0.00 126 767 88 0 0 0 0 0 0.00 0 0 10422 70 30 8 22 0 250 0 5 21 0 276 6 24 52.65 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10423 42 18 9 9 0 171 0 8 18 0 197 4 14 2.13 35.18 0 0 0 0 0 0 0 0 0.00 0 0 10424 282 119 72 47 0 0 0 0 0 0 0 65 54 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10425 87 37 15 22 0 0 0 0 15 0 15 15 22 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10426 38 16 6 10 0 83 0 7 29 0 119 6 10 16.37 3.60 0 0 0 0 0 0 0 0 0.00 0 0 10430 460 195 94 101 0 0 0 0 0 7 7 85 110 0.00 0.00 74 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-15
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10431 304 127 119 8 0 0 0 0 0 0 0 104 23 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10432 846 354 322 32 0 0 0 0 0 9 9 268 86 0.00 0.00 0 0 0 0 0 206 0 0 0.00 0 0 10433 79 33 27 6 0 0 0 0 0 12 12 27 6 0.00 0.00 116 0 0 0 0 0 0 0 0.00 0 0 10436 79 33 30 3 0 0 0 0 0 0 0 24 9 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10437 176 74 58 16 0 0 0 0 0 0 0 74 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10439 204 85 82 3 0 0 0 0 0 0 0 85 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10440 402 168 164 4 0 0 0 0 0 0 0 135 33 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10443 199 83 78 5 0 0 0 5 0 0 5 64 19 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10444 153 64 64 0 0 0 0 0 0 0 0 64 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10445 184 79 14 65 0 55 0 38 45 0 138 14 65 4.40 12.39 0 0 0 0 0 0 0 0 0.00 0 0 10446 798 344 0 344 0 20 0 0 177 0 197 344 0 9.19 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10449 183 79 2 77 0 914 0 409 536 0 1859 30 49 119.02 91.00 0 0 0 0 0 0 0 66 0.00 0 0 10450 967 408 266 142 0 0 0 0 0 7 7 259 149 0.00 0.00 74 0 0 0 0 0 0 0 0.00 0 0 10451 93 39 33 6 0 0 0 0 0 0 0 29 10 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10452 17 7 7 0 0 0 0 0 0 0 0 7 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10454 38 16 13 3 0 0 0 0 0 0 0 15 1 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10455 297 124 124 0 0 0 0 0 0 0 0 111 13 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10456 765 319 319 0 0 0 0 0 0 6 6 144 175 0.00 0.00 0 131 20 0 1388 0 0 0 0.00 0 0 10457 357 149 140 9 0 206 0 0 0 0 206 149 0 40.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10459 64 27 13 14 0 0 0 0 0 0 0 25 2 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10460 260 109 87 22 0 0 0 0 0 11 11 91 18 0.00 0.00 0 0 0 0 0 255 0 0 0.00 0 0 11006 2 1 1 0 0 3 0 0 23 0 26 0 1 0.00 3.62 0 0 0 0 0 0 0 0 0.00 0 0 11115 22 9 9 0 0 0 0 0 0 0 0 9 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 11170 264 111 88 23 0 0 0 0 0 0 0 75 36 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 11439 330 138 122 16 0 0 0 0 0 0 0 138 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 11450 150 63 55 8 0 0 0 0 0 0 0 52 11 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20030 0 0 0 0 0 27 34 18 168 0 248 0 0 0.793596 0 0 0 0 0 0 0 0 0 0 0 0 20042 0 0 0 0 1 81 31 2 8 0 123 0 0 244.7528 0 0 0 0 0 0 0 0 0 0 0 0 20043 4 2 0 1 0 42 32 33 12 0 120 2 10 273.547 0 0 0 0 0 0 0 0 0 0 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-16
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 20044 0 0 0 0 0 43 4 5 75 0 127 0 0 6.157002 0 0 0 0 0 0 0 0 0 0 0 0 20045 0 0 0 0 42 587 143 15 219 14 1021 11 0 3.121089 0 0 0 0 0 0 0 0 0 0 0 14 20086 0 0 0 0 303 0 0 0 0 5 308 0 0 0.00 0.00 45 0 0 0 0 0 0 0 0.55 0 0 20089 786 328 324 4 0 0 0 0 0 7 7 305 23 0.00 0.00 68 0 0 0 0 0 0 0 0.00 0 0 20092 736 308 276 32 0 0 0 0 0 3 3 259 49 0.00 0.00 26 0 0 0 0 0 0 0 0.00 0 0 20095 338 142 105 37 0 0 0 0 0 0 0 142 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20096 246 103 88 15 0 0 0 0 0 0 0 103 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20097 554 231 231 0 0 0 0 0 0 0 0 38 193 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20102 325 137 97 40 0 42 0 0 471 59 571 59 78 0.00 0.00 159 0 947 0 0 0 0 0 0.00 0 0 20103 136 57 43 14 0 0 0 0 0 12 12 57 0 0.00 0.00 0 63 43 170 0 0 0 0 0.00 0 0 20104 479 200 200 0 95 0 0 0 0 0 95 172 28 0.00 0.00 0 0 0 0 0 0 0 0 0.17 0 0 20105 290 121 121 0 216 0 0 0 0 0 216 105 16 0.00 0.00 0 0 0 0 0 0 0 0 0.39 0 0 20106 439 183 183 0 0 0 0 0 0 0 0 162 21 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20107 29 12 12 0 0 17 0 5 0 0 22 12 0 3.30 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20116 182 76 76 0 0 373 238 110 248 3 972 76 0 69.82 19.36 33 0 0 0 0 0 0 0 0.00 0 0 20118 0 0 0 0 0 0 0 0 0 5 5 0 0 0.00 0.00 47 0 0 0 0 0 0 0 0.00 0 0 20121 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20123 0 0 0 0 0 0 0 0 0 16 16 0 0 0.00 0.00 0 0 0 0 0 192 169 0 0.00 0 0 108503 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 109702 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-17
2035 Socioeconomic Data
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10002 118 47 47 0 0 0 0 0 0 0 0 47 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10003 488 195 195 0 0 0 0 0 0 0 0 195 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10004 448 167 5 162 0 15 0 0 1781 0 1796 28 139 0.91 20.35 0 0 0 0 0 0 0 292 0.00 0 0 10005 418 167 167 0 0 0 0 0 0 0 0 142 25 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10006 0 0 0 0 0 0 0 0 2706 0 2706 0 0 0.00 0.00 0 0 0 0 0 0 0 480 0.00 0 0 10007 25 10 10 0 0 0 0 0 0 0 0 6 4 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10008 395 155 118 37 0 0 0 0 0 0 0 155 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10009 2203 844 347 497 0 7 0 29 4 0 40 481 363 3.37 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10011 45 18 18 0 0 0 0 0 0 0 0 18 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10012 459 181 149 32 60 0 0 0 0 0 60 171 10 0.00 0.00 0 0 0 0 0 0 0 0 0.11 0 0 10013 709 266 28 238 0 7 6 0 0 0 14 64 202 1.44 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10014 820 310 69 241 0 62 97 5 262 0 426 137 173 12.41 0.00 0 0 0 0 0 0 0 46 0.00 0 0 10015 250 97 58 39 0 0 0 0 0 1 1 97 0 0.00 0.00 10 0 0 0 0 0 0 0 0.00 0 0 10016 0 0 0 0 0 14 0 1 59 0 74 0 0 3.08 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10017 895 358 358 0 0 0 0 0 1 0 1 301 57 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10018 1606 635 539 96 0 0 0 0 0 0 0 523 112 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10019 2515 954 253 701 0 125 0 12 115 13 266 509 445 27.72 0.00 20 0 0 0 0 175 79 0 0.00 0 0 10020 1731 678 488 190 0 0 0 0 0 32 32 678 0 0.00 0.00 0 269 463 0 0 0 0 0 0.00 0 0 10021 137 51 1 50 0 8 0 0 293 0 301 7 44 0.00 5.09 0 0 0 0 13560 0 0 49 0.00 0 0 10022 2187 850 521 329 0 70 0 10 131 0 211 700 150 20.85 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10023 1470 576 416 160 0 21 0 0 0 0 21 489 87 4.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10024 0 0 0 0 0 53 0 1 156 0 209 0 0 0.00 24.43 0 0 0 0 0 0 0 0 0.00 0 0 10025 968 364 54 310 0 0 0 0 0 0 0 278 86 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10026 0 0 0 0 0 0 130 0 0 0 130 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10027 0 0 0 0 0 0 0 0 0 24 24 0 0 0.00 0.00 0 485 74 0 0 0 0 0 0.00 0 0 10028 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-18
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10029 659 261 225 36 0 5 0 0 15 0 20 261 0 1.58 1.43 0 0 0 0 0 0 0 0 0.00 0 0 10033 97 38 29 9 0 107 0 48 208 0 363 38 0 23.44 14.44 0 0 0 0 0 0 7 0 0.00 0 0 10034 250 100 100 0 0 0 0 0 0 0 0 100 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10035 5 2 2 0 0 0 0 0 0 0 0 1 1 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10036 1163 443 143 300 0 0 0 67 0 0 67 443 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10037 718 268 12 256 0 0 0 0 0 0 0 63 205 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10038 423 169 169 0 0 0 0 0 0 21 21 169 0 0.00 0.00 0 422 68 0 0 0 0 0 0.00 0 0 10039 115 46 46 0 0 0 0 0 0 0 0 46 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10040 510 204 204 0 0 0 0 0 0 0 0 194 10 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10041 4104 1635 1555 80 0 0 0 0 0 0 0 1086 549 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10042 468 187 185 2 84 0 0 0 0 0 84 172 15 0.00 0.00 0 0 0 0 0 0 0 0 0.15 0 0 10043 948 373 289 84 0 21 0 9 283 19 331 233 140 6.02 2.10 189 0 0 0 0 0 0 0 0.00 0 0 10045 315 126 126 0 0 0 0 0 0 0 0 126 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10046 749 279 0 279 0 2591 0 83 140 0 2814 279 0 115.08 433.53 0 0 0 0 0 0 0 0 0.00 0 0 10048 323 128 114 14 0 0 0 0 0 101 101 128 0 0.00 0.00 13 0 0 2200 0 0 0 0 0.00 0 0 10049 148 55 0 55 0 1017 0 30 6 0 1053 34 21 193.95 7.00 0 0 0 0 0 0 0 0 0.00 0 0 10052 929 348 32 316 0 761 0 0 344 0 1105 100 248 130.06 28.80 0 0 0 0 0 0 0 52 0.00 0 0 10053 810 308 95 213 0 340 0 126 357 0 823 308 0 127.98 4.74 0 0 0 0 0 0 0 0 0.00 0 0 10054 13 5 5 0 0 94 0 0 376 0 470 5 0 14.29 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10055 385 149 80 69 0 0 0 14 0 0 14 75 74 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10056 193 76 59 17 0 0 0 0 0 0 0 55 21 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10057 165 64 39 25 46 24 0 0 2 0 71 31 33 5.52 0.00 0 0 0 0 0 0 0 0 0.08 0 0 10058 35 14 14 0 0 0 0 0 0 14 14 14 0 0.00 0.00 0 287 54 0 0 0 0 0 0.00 0 0 10060 1688 675 675 0 0 0 0 0 0 0 0 545 130 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10061 445 178 178 0 0 0 0 0 0 0 0 156 22 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10064 280 112 110 2 0 2 0 0 20 0 22 108 4 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10065 1005 401 391 10 0 0 0 0 0 25 25 347 54 0.00 0.00 60 385 73 0 0 0 0 0 0.00 0 0 10066 248 99 99 0 0 0 0 0 0 0 0 71 28 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-19
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10067 370 148 148 0 0 0 0 0 0 0 0 148 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10071 435 170 115 55 0 0 0 0 0 0 0 74 96 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10073 143 57 57 0 0 106 0 0 0 0 106 54 3 10.76 9.76 0 0 0 0 0 0 0 0 0.00 0 0 10074 350 140 140 0 0 10 36 0 0 0 46 85 55 2.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10075 140 56 56 0 0 0 0 0 0 0 0 50 6 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10076 317 125 101 24 0 4 37 0 34 0 76 52 73 5.34 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10077 358 143 143 0 0 0 0 0 0 0 0 143 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10078 0 0 0 0 0 3 0 30 35 0 68 0 0 0.68 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10079 40 16 16 0 0 0 0 0 0 0 0 12 4 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10080 0 0 0 0 0 197 0 92 86 0 375 0 0 71.87 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10081 325 125 56 69 0 3 0 0 2 0 5 79 46 0.68 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10082 553 206 0 206 0 599 0 32 28 0 659 103 103 112.60 15.44 0 0 0 0 0 0 0 0 0.00 0 0 10083 1167 456 314 142 0 0 0 0 68 0 68 244 212 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10084 567 215 58 157 0 7 0 0 107 31 145 99 116 0.00 0.00 0 369 89 0 0 0 257 0 0.00 0 0 10085 690 265 119 146 0 0 0 0 0 0 0 132 133 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10086 1259 485 238 247 0 3 0 30 39 15 87 352 133 0.68 0.00 0 296 50 0 0 0 0 0 0.00 0 0 10087 784 294 29 265 0 0 0 0 0 0 0 157 137 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10088 325 121 1 120 0 0 0 333 0 0 333 13 108 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10089 841 320 102 218 0 0 56 0 0 0 56 162 158 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10090 368 137 0 137 0 270 0 172 39 0 481 3 134 63.19 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10091 1476 583 486 97 0 0 0 0 0 0 0 421 162 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10092 610 228 15 213 0 35 0 224 386 0 645 101 127 2.79 13.38 0 0 0 0 0 0 0 53 0.00 0 0 10093 0 0 0 0 0 15 0 10 0 0 25 0 0 2.92 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10096 430 160 0 160 0 37 0 75 9 0 122 0 160 0.00 12.80 0 0 0 0 0 0 0 0 0.00 0 0 10097 86 32 0 32 0 55 0 173 235 0 462 0 32 2.36 22.25 0 0 0 0 0 0 0 31 0.00 0 0 10102 840 320 105 215 0 0 0 0 0 0 0 186 134 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10103 1037 411 363 48 0 0 0 0 26 6 32 411 0 0.00 0.00 62 0 0 0 0 0 0 0 0.00 0 0 10105 81 32 29 3 0 0 0 0 0 0 0 24 8 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-20
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10106 652 251 118 133 0 0 0 0 3 0 3 246 5 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10108 279 110 91 19 0 0 0 0 0 0 0 95 15 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10109 145 55 15 40 0 10 0 11 25 36 82 49 6 3.44 0.00 32 691 94 0 0 0 0 0 0.00 0 0 10110 0 0 0 0 0 168 0 0 0 0 168 0 0 0.00 32.64 0 0 0 0 0 0 0 0 0.00 0 0 10111 226 84 0 84 0 304 0 7 41 0 351 0 84 64.84 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10113 251 94 6 88 0 104 0 105 44 0 253 39 55 30.52 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10114 533 213 213 0 0 0 0 0 0 0 0 150 63 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10115 1196 478 478 0 0 0 0 0 0 0 0 478 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10116 824 325 264 61 0 0 0 0 279 0 279 317 8 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10117 2631 988 121 867 0 16 0 0 6 21 43 175 813 2.05 1.86 26 366 60 0 0 0 0 0 0.00 0 0 10118 1174 465 403 62 0 0 0 0 706 0 706 465 0 0.00 0.00 0 0 0 0 0 0 0 120 0.00 0 0 10119 549 212 112 100 0 0 0 0 0 0 0 128 84 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10120 827 316 120 196 0 0 0 0 0 53 53 126 190 0.00 0.00 0 1094 167 0 0 0 0 0 0.00 0 0 10121 302 118 82 36 0 82 0 0 701 6 789 118 0 6.36 0.00 0 0 0 0 0 86 44 0 0.00 0 0 10122 188 71 15 56 0 41 0 221 0 0 262 71 0 8.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10123 1535 595 342 253 0 0 0 69 22 0 91 561 34 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10124 999 381 131 250 0 0 0 0 4 99 103 133 248 0.00 0.00 0 0 0 2188 0 0 0 0 0.00 0 0 10125 0 0 0 0 0 431 0 172 239 0 842 0 0 98.06 46.52 0 0 0 0 0 0 0 0 0.00 0 0 10126 400 149 0 149 0 409 0 31 297 0 736 23 126 44.71 38.05 0 0 0 0 0 0 0 47 0.00 0 0 10127 381 144 33 111 0 0 0 0 7 0 7 40 104 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10128 0 0 0 0 0 379 0 154 26 0 559 0 0 50.40 30.10 0 0 0 0 0 0 0 0 0.00 0 0 10129 174 67 30 37 0 215 17 74 36 0 342 26 41 22.96 24.74 0 0 0 0 0 0 0 0 0.00 0 0 10130 367 137 3 134 0 99 4 6 21 0 130 51 86 24.46 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10131 115 43 0 43 0 86 0 229 3 0 317 0 43 17.89 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10132 112 43 20 23 0 16 0 0 4 0 21 21 22 2.94 2.66 0 0 0 0 0 0 0 0 0.00 0 0 10134 253 95 12 83 0 0 0 0 0 0 0 23 72 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10135 246 92 7 85 0 0 7 0 0 7 14 5 87 0.00 0.00 0 0 0 154 0 0 0 0 0.00 0 0 10136 198 74 5 69 0 0 0 0 5 0 5 17 57 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-21
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10137 144 54 5 49 0 25 18 46 28 0 117 5 49 7.81 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10138 0 0 0 0 0 0 0 369 0 0 369 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10139 302 113 10 103 0 18 0 147 261 0 427 35 78 6.24 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10140 27 10 1 9 0 109 8 106 22 0 246 10 0 14.20 19.90 0 0 0 0 0 0 0 0 0.00 0 0 10141 3 1 0 1 0 126 0 1 67 0 195 1 0 25.56 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10144 687 269 194 75 0 2 0 0 299 0 302 179 90 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10145 461 174 32 142 0 1 0 0 27 0 28 53 121 1.37 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10147 704 279 244 35 0 0 0 0 0 16 16 279 0 0.00 0.00 0 308 64 0 0 0 0 0 0.00 0 0 10148 799 299 20 279 0 0 0 137 0 0 137 48 251 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10150 155 58 2 56 0 0 0 56 33 0 89 0 58 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10151 1024 406 358 48 0 0 0 0 0 0 0 406 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10152 333 133 133 0 0 0 0 0 0 15 15 133 0 0.00 0.00 58 0 0 0 0 225 0 0 0.00 0 0 10153 0 0 0 0 0 1802 0 692 58 0 2552 0 0 77.34 279.10 0 0 0 0 0 0 0 0 0.00 0 0 10154 0 0 0 0 0 459 0 106 244 0 809 0 0 79.02 14.64 0 0 0 0 0 0 0 42 0.00 0 0 10155 212 80 16 64 0 0 0 0 0 0 0 12 68 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10156 416 156 15 141 0 0 0 0 48 0 48 22 134 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10157 791 301 93 208 0 0 0 0 85 0 85 109 192 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10158 11 4 0 4 0 207 0 35 1106 0 1348 2 2 17.54 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10159 479 179 9 170 0 61 0 11 42 0 114 2 177 1.88 9.76 0 0 0 0 0 0 0 0 0.00 0 0 10160 197 74 9 65 0 43 2 10 37 0 92 14 60 6.97 3.21 0 0 0 0 0 0 0 0 0.00 0 0 10161 286 109 37 72 0 27 0 73 48 0 149 63 46 6.38 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10162 702 263 25 238 0 0 0 0 0 0 0 66 197 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10163 444 166 10 156 0 15 0 50 34 2 100 16 150 7.36 0.00 15 0 0 0 0 0 0 0 0.00 0 0 10164 315 118 9 109 0 0 0 0 0 0 0 9 109 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10165 107 40 2 38 0 0 0 0 0 0 0 2 38 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10166 314 118 14 104 0 54 0 46 8 0 107 30 88 11.50 0.00 0 0 0 0 993 0 0 0 0.00 0 0 10167 952 362 111 251 0 91 0 1 43 3 137 131 231 22.28 1.19 25 0 0 0 0 0 0 0 0.00 0 0 10168 1717 648 124 524 0 5 0 12 22 6 44 117 531 4.44 0.00 60 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-22
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10169 464 183 151 32 0 0 0 0 0 0 0 126 57 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10170 617 242 181 61 94 0 0 0 0 0 94 242 0 0.00 0.00 0 0 0 0 0 0 0 0 0.17 0 0 10171 412 162 124 38 0 0 0 0 0 0 0 133 29 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10172 64 25 19 6 0 0 0 0 0 0 0 20 5 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10173 160 64 64 0 0 0 0 0 0 3 3 64 0 0.00 0.00 34 0 0 0 0 0 0 0 0.00 0 0 10174 233 90 45 45 0 1 0 0 1 0 2 90 0 0.26 0.24 0 0 0 0 0 0 0 0 0.00 0 0 10175 0 0 0 0 0 280 0 42 81 0 403 0 0 65.53 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10176 0 0 0 0 0 283 0 109 57 0 448 0 0 60.50 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10177 327 127 78 49 0 0 0 0 0 0 0 93 34 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10178 609 234 105 129 0 74 0 0 0 0 74 191 43 14.43 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10179 230 86 7 79 0 0 0 0 7 0 7 12 74 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10180 366 137 9 128 0 4 0 0 0 0 4 11 126 0.77 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10181 0 0 0 0 0 32 0 132 20 0 183 0 0 5.73 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10182 258 96 0 96 0 30 0 17 44 0 91 2 94 1.16 5.48 0 0 0 0 0 0 0 0 0.00 0 0 10183 127 48 13 35 0 0 0 0 11 3 13 9 39 0.00 0.00 26 0 0 0 0 0 0 0 0.00 0 0 10184 648 257 231 26 0 1 0 0 7 0 8 257 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10185 321 125 82 43 0 0 0 0 0 18 18 125 0 0.00 0.00 0 359 64 0 0 0 0 0 0.00 0 0 10186 701 274 190 84 0 0 0 0 0 0 0 238 36 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10187 777 296 97 199 0 0 0 0 0 0 0 89 207 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10188 64 24 4 20 0 25 0 113 229 9 375 1 23 0.00 0.00 85 0 0 0 0 0 0 0 0.00 0 0 10189 693 261 43 218 0 95 5 20 11 0 132 35 226 13.49 11.33 0 0 0 0 0 0 0 0 0.00 0 0 10190 215 80 0 80 0 6 0 268 2 0 275 1 79 2.07 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10193 393 147 11 136 0 0 0 251 24 0 275 16 131 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10194 0 0 0 0 0 138 0 0 3688 0 3826 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10195 1006 390 226 164 0 10 0 0 3 0 13 390 0 2.36 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10196 16 6 1 5 0 5 0 59 209 0 272 0 6 0.37 1.22 0 0 0 0 0 0 0 37 0.00 0 0 10199 741 277 18 259 0 3 0 0 2 2 7 38 239 1.61 0.00 19 0 0 0 0 0 0 0 0.00 0 0 10200 13 5 1 4 0 1 0 214 0 0 215 3 2 0.37 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-23
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10201 108 41 10 31 0 0 0 49 0 0 49 8 33 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10202 284 106 6 100 0 0 0 11 0 0 11 20 86 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10203 3 1 1 0 0 0 0 39 2279 0 2317 1 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10204 524 201 84 117 0 0 0 0 11 0 11 101 100 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10205 413 154 3 151 0 110 0 40 46 0 196 154 0 28.09 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10206 219 83 21 62 0 0 0 0 0 0 0 62 21 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10208 321 120 9 111 0 0 0 0 0 0 0 21 99 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10209 1803 674 38 636 0 43 0 31 16 0 90 76 598 11.60 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10211 312 118 27 91 0 11 0 7 16 0 34 47 71 5.71 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10212 345 130 21 109 0 0 0 0 0 0 0 29 101 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10213 324 121 5 116 0 0 0 5 24 0 29 23 98 0.04 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10214 333 126 27 99 0 0 0 0 0 0 0 63 63 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10215 279 105 15 90 0 0 0 0 25 0 25 21 84 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10216 344 130 29 101 0 0 0 0 0 0 0 20 110 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10217 579 217 18 199 0 2 0 0 1 0 3 21 196 0.54 0.49 0 0 0 0 0 0 0 0 0.00 0 0 10218 11 4 0 4 0 110 17 142 324 0 594 2 2 9.55 24.39 0 0 0 0 0 0 0 0 0.00 0 0 10219 529 197 1 196 0 64 0 174 1 0 240 2 195 12.64 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10221 724 270 4 266 0 51 0 4 3 0 57 16 254 11.10 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10222 115 43 1 42 0 164 7 74 13 0 258 29 14 33.84 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10223 291 109 10 99 0 21 0 14 2 0 37 5 104 4.87 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10224 211 80 21 59 0 0 0 0 0 7 7 14 66 0.00 0.00 66 0 0 0 0 0 0 0 0.00 0 0 10225 126 47 1 46 0 194 3 29 46 0 272 3 44 43.36 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10226 296 111 11 100 0 81 0 15 24 0 120 13 98 19.64 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10228 289 108 3 105 0 23 0 45 0 0 67 8 100 4.38 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10229 233 87 5 82 0 0 0 0 22 0 22 17 70 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10230 336 126 11 115 0 16 7 54 16 0 92 31 95 6.19 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10231 0 0 0 0 0 277 0 54 9 0 340 0 0 9.89 48.19 0 0 0 0 0 0 0 0 0.00 0 0 10232 0 0 0 0 0 404 0 91 48 0 544 0 0 81.82 6.02 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-24
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10234 78 30 12 18 0 0 0 0 0 0 0 24 6 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10235 108 41 10 31 0 0 0 189 15 0 204 6 35 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10236 214 81 19 62 0 0 0 0 0 0 0 16 65 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10237 301 113 12 101 0 0 0 0 0 0 0 5 108 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10238 53 21 21 0 0 0 0 0 0 0 0 21 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10239 53 21 21 0 0 0 0 0 0 0 0 21 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10240 164 63 30 33 0 0 0 22 0 4 26 63 0 0.00 0.00 40 0 0 0 0 0 0 0 0.00 0 0 10241 167 63 11 52 0 5 0 96 0 0 101 51 12 0.00 0.99 0 0 0 0 0 0 0 0 0.00 0 0 10242 41 16 9 7 0 22 0 46 129 0 197 5 11 0.00 5.88 0 0 0 0 0 0 0 22 0.00 0 0 10243 102 39 16 23 0 0 0 0 0 0 0 28 11 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10244 150 56 2 54 0 27 0 204 42 0 272 29 27 3.04 7.72 0 0 0 0 0 0 0 0 0.00 0 0 10245 48 18 4 14 0 44 0 101 121 0 267 0 18 0.98 9.10 0 0 0 0 0 0 0 0 0.00 0 0 10246 96 36 6 30 0 17 0 45 461 0 523 4 32 0.45 1.80 0 0 0 0 0 0 0 73 0.00 0 0 10247 314 117 3 114 0 0 0 0 0 0 0 70 47 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10248 185 69 4 65 0 27 0 43 49 3 122 28 41 2.41 2.49 26 0 0 0 0 0 0 0 0.00 0 0 10249 104 39 4 35 0 3 0 0 66 0 69 7 32 2.36 2.14 0 0 0 0 0 0 0 0 0.00 0 0 10250 59 23 16 7 0 0 0 0 0 0 0 20 3 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10251 50 20 20 0 0 0 0 0 0 0 0 20 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10252 43 17 17 0 0 0 0 0 0 0 0 17 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10253 126 47 2 45 0 46 11 119 4 0 180 2 45 10.91 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10254 235 88 6 82 0 0 0 0 0 0 0 9 79 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10255 302 113 9 104 0 0 0 0 0 0 0 16 97 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10256 283 106 10 96 0 0 0 0 0 0 0 19 87 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10257 199 75 11 64 0 0 0 0 13 0 13 13 62 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10258 239 90 15 75 0 0 0 18 20 0 38 28 62 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10259 195 73 6 67 0 19 0 79 0 0 98 11 62 3.70 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10260 216 81 10 71 0 0 0 0 0 0 0 5 76 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10261 248 93 9 84 0 0 0 0 4 0 4 14 79 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-25
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10262 237 89 13 76 0 0 0 0 0 0 0 30 59 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10263 215 81 14 67 0 0 0 21 2 0 23 23 58 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10264 56 21 2 19 0 122 24 10 39 0 195 1 20 25.42 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10265 300 112 5 107 0 0 0 0 0 0 0 7 105 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10266 142 53 0 53 0 45 0 54 59 0 158 16 37 8.29 5.30 0 0 0 0 0 0 0 0 0.00 0 0 10267 56 22 15 7 0 14 0 20 52 0 87 22 0 0.96 3.74 0 0 0 0 0 0 0 0 0.00 0 0 10268 96 37 19 18 0 0 0 0 74 0 74 29 8 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10269 91 36 31 5 0 0 0 0 0 0 0 35 1 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10270 58 23 19 4 0 0 0 0 0 0 0 23 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10271 65 26 26 0 0 0 0 0 114 0 114 20 6 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10272 35 14 14 0 0 0 0 0 0 0 0 11 3 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10273 257 96 2 94 0 140 0 43 34 0 217 30 66 26.77 10.17 0 0 0 0 0 0 0 0 0.00 0 0 10274 51 19 0 19 0 469 0 33 123 0 625 0 19 44.63 64.45 0 0 0 0 87 0 0 0 0.00 0 0 10275 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10276 11 4 0 4 0 12 0 34 55 7 108 3 1 2.24 0.00 0 0 0 0 0 171 0 0 0.00 0 0 10277 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10278 0 0 0 0 0 0 0 0 0 1 1 0 0 0.00 0.00 13 0 0 0 0 0 0 0 0.00 0 0 10279 0 0 0 0 0 22 0 44 189 0 255 0 0 1.67 3.30 0 0 0 0 0 0 0 0 0.00 0 0 10280 150 56 1 55 0 2 0 164 17 0 183 13 43 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10281 209 78 1 77 0 52 0 58 0 0 110 3 75 10.06 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10282 86 32 0 32 0 117 0 171 33 0 321 0 32 17.19 16.11 0 0 0 0 0 0 0 0 0.00 0 0 10283 215 80 0 80 0 865 0 20 59 0 945 46 34 93.52 81.98 0 0 0 0 0 0 0 0 0.00 0 0 10284 134 50 4 46 0 0 0 85 59 1 146 6 44 0.00 0.00 0 0 0 0 0 30 0 0 0.00 0 0 10285 21 8 2 6 0 0 0 34 31 0 65 1 7 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10286 16 6 0 6 0 30 0 76 17 0 124 1 5 8.25 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10287 432 161 4 157 0 0 0 0 0 0 0 6 155 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10288 632 241 82 159 0 0 0 0 0 0 0 81 160 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10289 115 44 17 27 0 0 0 0 0 0 0 34 10 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-26
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10290 77 30 17 13 0 0 0 0 0 0 0 19 11 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10292 818 322 254 68 0 0 0 0 0 0 0 248 74 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10293 893 357 357 0 0 1 0 0 65 35 101 357 0 0.00 0.00 0 646 109 0 0 84 0 0 0.00 0 0 10294 731 290 258 32 0 42 0 11 14 0 67 290 0 10.02 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10295 8 3 0 3 0 115 0 30 65 0 210 3 0 34.26 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10296 0 0 0 0 0 158 0 6 54 0 218 0 0 29.27 3.46 0 0 0 0 0 0 0 0 0.00 0 0 10297 378 142 17 125 0 7 0 13 0 0 20 33 109 0.71 0.64 0 0 0 0 0 0 0 0 0.00 0 0 10298 16 6 0 6 0 153 133 1 80 0 368 6 0 50.95 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10299 63 24 10 14 0 47 274 28 48 0 397 8 16 14.16 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10300 0 0 0 0 0 199 915 101 155 0 1371 0 0 20.87 20.87 0 0 0 0 0 0 0 0 0.00 0 0 10301 3 1 1 0 0 24 0 79 0 5 108 0 1 4.75 0.00 0 0 0 114 0 0 0 0 0.00 0 0 10302 10 4 4 0 0 48 166 11 130 0 355 4 0 8.08 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10303 303 113 1 112 0 34 4 93 30 0 160 25 88 15.62 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10304 3 1 1 0 0 8 0 181 88 0 277 1 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10305 32 12 1 11 0 3 0 297 95 0 395 1 11 0.00 2.53 0 0 0 0 0 0 0 0 0.00 0 0 10306 487 188 98 90 0 110 32 9 90 0 242 119 69 36.23 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10307 82 31 5 26 0 98 0 10 26 0 134 5 26 22.46 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10308 257 98 35 63 0 0 0 0 0 0 0 34 64 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10309 34 13 6 7 0 98 62 128 46 0 334 0 13 29.40 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10310 80 30 1 29 0 125 80 77 22 0 304 7 23 28.94 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10311 448 167 0 167 0 0 0 0 0 0 0 167 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10312 0 0 0 0 0 0 82 339 0 0 421 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10313 0 0 0 0 0 54 17 0 483 0 554 0 0 23.60 21.40 0 0 0 0 0 0 0 0 0.00 0 0 10314 571 216 48 168 0 0 0 0 11 0 11 80 136 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10315 882 331 39 292 0 15 1 0 4 0 20 200 131 3.40 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10316 0 0 0 0 0 0 0 49 0 0 49 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10317 198 74 2 72 0 3 0 122 8 2 135 28 46 2.02 0.00 0 0 0 0 0 18 26 0 0.00 0 0 10319 3 1 0 1 0 247 104 99 168 0 618 1 0 69.05 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-27
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10320 118 45 16 29 0 77 111 20 59 0 267 35 10 20.78 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10321 133 51 23 28 0 34 0 0 0 0 34 38 13 6.57 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10322 13 5 5 0 0 69 0 8 48 0 125 4 1 19.87 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10323 0 0 0 0 0 2 62 132 19 0 215 0 0 1.45 1.45 0 0 0 0 0 0 0 0 0.00 0 0 10324 31 12 5 7 0 0 0 0 0 0 0 2 10 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10325 795 310 202 108 0 0 0 0 0 0 0 224 86 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10326 448 171 62 109 0 0 0 0 0 0 0 114 57 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10327 0 0 0 0 11 136 257 2 30 0 435 0 0 34.94 0.00 0 0 0 0 0 0 0 0 0.02 0 0 10328 0 0 0 0 0 39 0 0 15 0 53 0 0 7.53 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10329 64 24 1 23 0 11 21 70 98 0 201 1 23 0.23 0.23 0 0 0 0 0 0 0 0 0.00 0 0 10330 0 0 0 0 0 0 0 0 503 0 503 0 0 0.00 0.00 0 0 0 0 0 0 0 89 0.00 0 0 10331 39 15 5 10 0 90 43 26 8 0 168 7 8 21.34 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10332 0 0 0 0 0 113 45 48 72 0 278 0 0 31.01 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10333 777 290 9 281 0 66 0 0 606 0 673 13 277 1.22 24.27 0 0 0 0 0 0 0 100 0.00 0 0 10334 252 94 1 93 0 330 0 4 57 0 391 0 94 63.12 6.72 0 0 0 0 0 0 0 0 0.00 0 0 10335 323 121 11 110 0 37 0 0 5 0 43 7 114 7.25 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10336 89 33 0 33 0 213 0 17 22 0 252 26 7 44.41 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10337 101 38 4 34 0 86 15 97 5 0 202 14 24 17.63 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10338 249 93 5 88 0 8 0 23 103 0 134 15 78 4.06 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10339 191 72 11 61 0 16 7 33 5 0 61 8 64 4.04 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10340 279 104 3 101 0 0 0 0 0 0 0 3 101 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10341 136 51 6 45 0 89 35 1 83 4 212 15 36 26.81 0.00 40 0 0 0 0 0 0 0 0.00 0 0 10342 397 148 1 147 0 187 30 0 21 0 238 0 148 37.78 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10343 117 44 9 35 0 64 54 136 53 0 307 20 24 24.23 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10344 21 8 5 3 0 265 106 28 170 0 569 8 0 57.10 13.46 0 0 0 0 0 0 0 0 0.00 0 0 10345 0 0 0 0 0 60 0 0 163 0 223 0 0 13.05 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10346 483 180 2 178 0 980 268 113 1336 0 2696 5 175 192.15 0.00 0 0 0 0 0 0 0 236 0.00 0 0 10347 225 84 3 81 0 93 24 22 27 0 166 23 61 20.12 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-28
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10348 11 4 0 4 0 143 0 13 8 0 164 1 3 31.43 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10349 81 30 0 30 0 3 0 0 823 0 825 27 3 0.19 3.63 0 0 0 0 0 0 0 142 0.00 0 0 10350 338 126 3 123 0 197 31 6 137 0 370 14 112 54.80 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10351 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10353 99 37 0 37 0 67 6 0 213 0 286 13 24 9.16 2.65 0 0 0 0 0 0 0 28 0.00 0 0 10355 240 90 7 83 0 3 0 0 45 0 49 24 66 1.12 1.47 0 0 0 0 0 0 0 0 0.00 0 0 10356 268 100 2 98 0 15 0 2 144 0 162 13 87 1.26 7.02 0 0 0 0 0 0 0 0 0.00 0 0 10357 61 23 6 17 0 146 0 86 19 0 251 5 18 30.28 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10358 0 0 0 0 0 676 0 276 403 0 1355 0 0 129.85 27.86 0 0 0 0 0 0 0 30 0.00 0 0 10359 0 0 0 0 0 222 0 45 425 0 692 0 0 69.90 0.00 0 0 0 0 120 0 0 32 0.00 0 0 10360 127 48 11 37 0 64 0 22 45 0 131 30 18 14.43 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10361 45 17 2 15 0 51 1 61 195 0 308 1 16 8.42 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10362 67 25 0 25 0 168 0 85 71 0 325 25 0 54.85 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10363 0 0 0 0 0 398 10 0 32 0 440 0 0 61.62 20.02 0 0 0 0 0 0 0 0 0.00 0 0 10364 67 25 0 25 0 212 0 50 286 0 547 0 25 49.08 0.00 0 0 0 0 0 0 0 38 0.00 0 0 10365 0 0 0 0 0 302 15 28 25 0 370 0 0 68.17 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10366 88 33 5 28 0 100 0 40 38 0 178 4 29 23.95 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10367 148 55 0 55 0 211 0 70 333 0 615 16 39 44.69 16.28 0 0 0 0 0 0 0 30 0.00 0 0 10368 126 47 2 45 0 82 30 260 23 0 395 44 3 15.40 0.00 0 0 0 0 361 0 0 0 0.00 0 0 10369 109 41 6 35 0 56 0 16 1009 5 1086 23 18 5.93 14.27 47 0 0 0 0 0 0 145 0.00 0 0 10370 0 0 0 0 0 319 49 0 53 0 421 0 0 11.44 57.78 0 0 0 0 0 0 0 0 0.00 0 0 10371 0 0 0 0 0 303 9 0 8 0 320 0 0 62.81 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10373 58 22 4 18 0 43 88 12 24 0 168 14 8 11.85 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10374 0 0 0 0 0 56 36 26 40 0 157 0 0 11.33 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10375 126 47 0 47 0 31 0 0 0 0 31 47 0 6.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10377 29 11 4 7 0 136 77 5 38 0 255 3 8 36.73 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10378 86 32 0 32 0 133 96 85 21 0 335 15 17 32.26 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10380 137 51 0 51 0 68 0 37 15 0 121 29 22 22.38 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-29
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10381 3 1 1 0 0 0 0 0 0 0 0 0 1 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10382 136 51 4 47 0 0 0 0 0 0 0 6 45 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10383 757 298 234 64 0 5 0 0 9 0 13 269 29 2.09 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10384 729 274 36 238 0 44 54 10 10 0 118 31 243 13.04 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10386 817 312 116 196 0 2 0 4 26 7 39 125 187 0.00 0.00 0 0 0 0 0 130 37 0 0.00 0 0 10387 207 78 12 66 0 0 0 0 0 0 0 19 59 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10388 199 76 29 47 0 62 0 6 5 0 73 42 34 14.71 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10389 244 91 3 88 0 0 0 15 0 0 15 3 88 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10390 120 45 5 40 0 0 0 0 7 0 7 13 32 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10391 281 105 4 101 0 3 0 97 7 0 107 25 80 0.50 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10392 528 198 19 179 0 0 0 0 0 0 0 30 168 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10393 356 134 20 114 0 0 0 0 0 0 0 97 37 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10394 232 87 11 76 0 0 0 39 0 0 39 6 81 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10395 128 49 19 30 0 0 0 222 0 1 224 11 38 0.00 0.00 13 0 0 0 0 0 0 0 0.00 0 0 10396 118 44 1 43 0 11 10 120 0 0 141 14 30 2.12 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10397 5 2 2 0 0 9 0 214 9 0 231 2 0 2.81 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10398 203 76 4 72 0 0 0 68 6 0 74 14 62 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10399 182 68 1 67 0 23 0 16 0 0 39 12 56 4.53 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10400 249 93 3 90 0 135 192 0 3 34 364 53 40 27.61 0.00 0 0 751 0 0 0 0 0 0.00 0 0 10401 250 93 1 92 0 50 9 7 59 0 124 23 70 11.36 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10402 70 27 11 16 0 74 50 10 45 0 179 5 22 25.37 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10403 327 122 2 120 0 81 0 13 49 0 143 0 122 29.69 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10405 188 71 13 58 0 0 0 0 0 0 0 8 63 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10406 110 41 3 38 0 129 143 14 22 0 308 5 36 27.72 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10407 47 18 9 9 0 103 98 3 63 0 266 12 6 41.61 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10408 81 31 13 18 0 51 79 31 3 0 165 25 6 11.69 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10409 42 16 5 11 0 238 0 0 0 0 239 4 12 46.44 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10410 11 4 1 3 0 211 324 16 279 0 830 0 4 66.18 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-30
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10412 179 68 18 50 0 79 0 0 0 0 79 18 50 8.11 7.36 0 0 0 0 0 0 0 0 0.00 0 0 10414 44 17 11 6 0 32 91 14 0 0 137 6 11 6.23 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10415 24 9 0 9 0 63 91 14 38 0 206 0 9 17.08 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10416 0 0 0 0 0 39 0 3 14 0 56 0 0 13.53 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10419 328 123 15 108 0 0 0 0 0 0 0 13 110 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10420 728 279 117 162 0 0 0 0 0 0 0 142 137 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10421 601 230 91 139 0 13 0 0 114 49 175 87 143 0.00 0.00 127 772 88 0 0 0 0 0 0.00 0 0 10422 326 122 9 113 0 250 0 5 47 0 302 25 97 52.65 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10423 55 21 10 11 0 171 0 8 18 0 197 4 17 2.13 35.18 0 0 0 0 0 0 0 0 0.00 0 0 10424 378 146 77 69 0 0 0 0 0 0 0 80 66 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10425 110 42 16 26 0 0 0 0 15 0 15 17 25 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10426 45 17 6 11 0 83 0 7 29 0 119 5 12 16.37 3.60 0 0 0 0 0 0 0 0 0.00 0 0 10430 532 205 99 106 0 0 0 0 0 7 7 88 117 0.00 0.00 74 0 0 0 0 0 0 0 0.00 0 0 10431 329 131 123 8 0 0 0 0 0 0 0 110 21 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10432 914 363 331 32 0 0 0 0 0 9 9 279 84 0.00 0.00 0 0 0 0 0 207 0 0 0.00 0 0 10433 84 33 27 6 0 0 0 0 0 12 12 27 6 0.00 0.00 117 0 0 0 0 0 0 0 0.00 0 0 10436 83 33 30 3 0 0 0 0 0 0 0 24 9 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10437 193 76 60 16 0 0 0 0 0 0 0 76 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10439 216 86 83 3 0 0 0 0 0 0 0 86 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10440 421 168 164 4 0 4 0 0 0 0 4 135 33 0.87 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10443 209 83 78 5 0 0 0 5 0 0 5 64 19 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10444 165 66 66 0 0 0 0 0 0 0 0 66 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10445 231 87 15 72 0 55 0 38 45 0 138 15 72 4.40 12.39 0 0 0 0 0 0 0 0 0.00 0 0 10446 983 366 0 366 0 20 0 0 177 0 197 366 0 4.60 4.60 0 0 0 0 0 0 0 0 0.00 0 0 10449 225 84 2 82 0 916 0 410 538 0 1864 32 52 139.65 70.88 0 0 0 0 0 0 0 66 0.00 0 0 10450 1044 407 265 142 0 0 0 0 0 7 7 259 148 0.00 0.00 74 0 0 0 0 0 0 0 0.00 0 0 10451 99 39 33 6 0 0 0 0 0 0 0 29 10 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10452 18 7 7 0 0 0 0 0 0 0 0 7 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-31
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 10454 41 16 13 3 0 0 0 0 0 0 0 15 1 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10455 330 132 132 0 0 0 0 0 0 0 0 120 12 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10456 815 326 326 0 0 0 0 0 0 6 6 153 173 0.00 0.00 0 132 20 0 1586 0 0 0 0.00 0 0 10457 372 148 139 9 0 206 0 0 0 0 206 148 0 40.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10459 70 27 13 14 0 0 0 0 0 0 0 25 2 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 10460 277 109 87 22 0 0 0 0 0 11 11 91 18 0.00 0.00 0 0 0 0 0 256 0 0 0.00 0 0 11006 3 1 1 0 0 3 0 0 23 0 26 0 1 0.00 3.62 0 0 0 0 0 0 0 0 0.00 0 0 11115 25 10 10 0 0 0 0 0 0 0 0 10 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 11170 302 119 94 25 0 0 0 0 0 0 0 80 39 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 11439 346 137 121 16 0 0 0 0 0 0 0 137 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 11450 138 62 54 8 0 3 0 2 7 0 12 52 7 0.45 0 0 0 0 0 0 0 0 0 0 0 0 20030 0 0 0 0 0 27 34 18 168 0 248 0 0 0.79 0 0 0 0 0 0 0 0 0 0 0 0 20042 0 0 0 0 1 88 34 3 9 0 134 0 0 242.78 0 0 0 0 0 0 0 0 0 0 0 0 20043 4 2 0 1 0 42 32 33 12 0 120 2 10 273.55 0 0 0 0 0 0 0 0 0 0 0 0 20044 0 0 0 0 0 43 4 5 75 0 127 0 0 6.11 0 0 0 0 0 0 0 0 0 0 0 0 20045 49 32 32 0 0 770 175 18 267 14 1244 11 27 3.12 0 0 0 0 0 0 0 0 0 0 0 14 20086 0 0 0 0 303 0 0 0 0 5 308 0 0 0.00 0.00 46 0 0 0 0 0 0 0 0.55 0 0 20089 964 385 381 4 0 0 0 0 0 7 7 345 40 0.00 0.00 68 0 0 0 0 0 0 0 0.00 0 0 20092 784 311 279 32 0 0 0 0 0 3 3 263 48 0.00 0.00 26 0 0 0 0 0 0 0 0.00 0 0 20095 364 143 106 37 0 0 0 0 0 0 0 143 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20096 354 140 120 20 0 0 0 0 0 0 0 140 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20097 578 231 231 0 0 0 0 0 0 0 0 38 193 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20102 368 144 101 43 0 42 0 0 471 59 571 64 80 0.00 0.00 160 0 947 0 0 0 0 0 0.00 0 0 20103 145 57 43 14 0 0 0 0 0 12 12 57 0 0.00 0.00 0 63 43 170 0 0 0 0 0.00 0 0 20104 500 200 200 0 95 0 0 0 0 0 95 172 28 0.00 0.00 0 0 0 0 0 0 0 0 0.17 0 0 20105 303 121 121 0 216 0 0 0 0 0 216 105 16 0.00 0.00 0 0 0 0 0 0 0 0 0.39 0 0 20106 455 182 182 0 0 0 0 0 0 0 0 161 21 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20107 30 12 12 0 0 17 0 5 0 0 22 12 0 3.30 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix LU-32
Final_IDPopulationHouseholdsSingle_Family_HHMulti_Family_HHAgricultural_EmploymentCommercial_EmploymentIndustrial_EmploymentOffice_EmploymentService_EmploymentEducation_EmploymentTotal_EmploymentLow_Density_ResidentialHigh_Density_ResidentialLow_Density_CommercialHigh_Density_Commercial PreSchool_StudentsElementary_EnrollmentMiddle_School_EnrollmentHigh_School_EnrollmentCollege_EnrollmentPrivate_Elementary_EnrollmentPrivate_High_EnrollmentHotels_MotelsAgriculturalUCSBFaculty_Staff 20116 198 79 79 0 0 373 238 110 248 3 972 79 0 69.82 19.36 33 0 0 0 0 0 0 0 0.00 0 0 20118 0 0 0 0 0 0 0 0 0 5 5 0 0 0.00 0.00 48 0 0 0 0 0 0 0 0.00 0 0 20121 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 20123 0 0 0 0 0 0 0 0 0 16 16 0 0 0.00 0.00 0 0 0 0 0 193 169 0 0.00 0 0 108503 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 109702 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.00 0 0 ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix OD-1
APPENDIX D – SB 375 VEHICLE O-D SUMMARIES
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix OD-2
2020 Base Year
Santa Barbara Goleta Santa Maria Lompoc Carpinteria Solvang Buelton Guadalupe Unincorporate
d
Ventura
County
San Luis
Obispo County Total
Santa Barbara 184,858 28,311 4,343 1,428 8,560 824 1,130 176 62,303 11,946 881 304,760 Goleta 28,348 39,750 11,899 2,918 1,587 462 1,901 84 32,696 774 789 121,208 Santa Maria 4,361 11,869 339,421 2,309 141 968 835 6,393 20,780 112 30,200 417,389 Lompoc 1,435 2,925 2,306 85,365 48 596 1,377 75 16,084 13 1,526 111,751 Carpinteria 8,598 1,595 139 47 18,919 59 70 13 7,311 5,894 42 42,688 Solvang 828 463 967 598 60 6,623 1,323 34 5,612 5 321 16,832 Buelton 1,129 1,900 836 1,387 71 1,324 3,941 31 3,816 3 264 14,701 Guadalupe 177 84 6,403 76 13 34 31 7,938 958 0 487 16,202 Unincorporated 62,386 32,719 20,736 16,069 7,306 5,607 3,811 955 89,295 2,083 2,211 243,178 Ventura County 11,944 782 107 13 5,896 5 3 0 2,089 - 2,759 23,598 San Luis Obispo County 888 781 30,207 1,526 43 321 264 487 2,205 1,776 - 38,499 Total 304,953 121,179 417,365 111,736 42,643 16,823 14,685 16,186 243,150 22,606 39,480 1,350,807 Santa Barbara Goleta Santa Maria Lompoc Carpinteria Solvang Buelton Guadalupe Unincorporate
d
Ventura
County
San Luis
Obispo County Total
Santa Barbara 424,301 241,767 266,238 73,934 102,930 27,378 44,619 12,285 537,726 381,837 73,055 2,186,070 Goleta 241,932 93,093 741,890 132,221 34,470 14,033 65,189 6,021 305,897 31,134 66,794 1,732,675 Santa Maria 268,549 741,900 961,968 51,132 10,402 32,074 25,227 61,294 692,274 10,502 550,040 3,405,361 Lompoc 74,231 132,289 50,896 131,325 3,191 12,855 24,985 2,097 179,051 1,103 67,195 679,218 Carpinteria 103,951 34,808 10,272 3,159 24,357 2,738 3,766 1,096 84,351 113,811 3,817 386,126 Solvang 27,558 14,099 32,020 12,889 2,765 6,031 5,660 1,449 58,121 306 17,519 178,417 Buelton 45,615 65,099 25,393 25,153 3,893 5,668 4,072 1,229 59,839 186 13,668 249,816 Guadalupe 12,377 6,041 61,403 2,104 1,113 1,460 1,226 8,528 17,795 5 4,252 116,302 Unincorporated 539,029 307,199 690,281 176,739 84,221 58,100 59,695 17,771 621,210 60,378 122,977 2,737,601 Ventura County 380,493 31,489 9,972 1,113 113,212 319 193 5 60,320 - 406,323 1,003,439 San Luis Obispo County 73,637 66,067 545,163 66,918 3,893 17,501 13,616 4,248 122,394 248,395 - 1,161,830 Total 2,191,671 1,733,851 3,395,497 676,688 384,447 178,157 248,247 116,022 2,738,978 847,657 1,325,641 13,836,855 Santa Barbara Goleta Santa Maria Lompoc Carpinteria Solvang Buelton Guadalupe Unincorporate
d
Ventura
County
San Luis
Obispo County Total
Santa Barbara 5,673 388 47 16 106 13 8 2 777 - - 7,032
Goleta 388 1,639 26 11 27 8 5 1 621 - - 2,727
Santa Maria 47 26 6,267 83 10 17 16 63 276 - - 6,805
Lompoc 16 11 83 1,282 3 9 10 3 150 - - 1,568
Carpinteria 106 27 10 3 677 2 1 0 147 - - 975
Solvang 13 8 17 9 2 243 30 1 107 - - 430
Buelton 8 5 16 10 1 30 172 1 81 - - 323
Guadalupe 2 1 63 3 0 1 1 130 12 - - 215
Unincorporated 777 621 276 150 147 107 81 12 1,878 - - 4,051 Ventura County - - - - - - - - - - - -
San Luis Obispo County - - - - - - - - - - - -
Total 7,032 2,727 6,805 1,568 975 430 323 215 4,051 - - 24,125 Santa Barbara Goleta Santa Maria Lompoc Carpinteria Solvang Buelton Guadalupe Unincorporate
d
Ventura
County
San Luis
Obispo County Total
Santa Barbara 8,839 3,029 2,914 867 1,341 429 319 148 6,065 - - 23,951 Goleta 2,980 2,738 1,585 516 587 251 185 80 3,516 - - 12,438 Santa Maria 2,914 1,586 15,602 1,882 721 571 484 605 6,034 - - 30,400 Lompoc 867 516 1,877 1,642 212 200 176 97 1,951 - - 7,537 Carpinteria 1,347 593 722 212 793 98 73 37 1,398 - - 5,273 Solvang 429 251 570 200 98 200 118 28 895 - - 2,788
Buelton 330 185 486 176 75 119 171 24 688 - - 2,253
Guadalupe 148 80 605 97 37 28 24 129 308 - - 1,456
Unincorporated 6,077 3,569 6,048 1,950 1,398 895 685 308 10,315 - - 31,246 Ventura County - - - - - - - - - - - -
San Luis Obispo County - - - - - - - - - - - -
Total 23,932 12,548 30,409 7,541 5,262 2,790 2,235 1,456 31,169 - - 117,342 CITIES
Daily Truck VMT
CITIES
Daily Auto VMT
Daily Truck Trips
CITIES
Daily Auto Trips
CITIES
Note that highlighted cells represent “through trips” within the City, thus trips not having an origin or destination within Santa Barbara ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix OD-3
2035 Forecast Year
Santa Barbara Goleta Santa Maria Lompoc Carpinteria Solvang Buelton Guadalupe Unincorporate
d
Ventura
County
San Luis
Obispo County Total
Santa Barbara 230,139 31,794 6,327 1,329 9,082 773 1,079 192 67,133 23,323 812 371,984 Goleta 31,836 44,374 9,789 3,096 1,657 337 1,808 89 34,849 1,678 1,009 130,522 Santa Maria 6,373 9,778 411,306 2,502 216 1,030 841 7,526 24,365 177 33,065 497,180 Lompoc 1,345 3,102 2,503 92,052 56 629 1,549 77 17,463 21 1,702 120,499 Carpinteria 9,132 1,669 213 55 20,848 62 81 15 7,931 12,064 47 52,116 Solvang 779 338 1,027 630 62 7,107 1,381 37 6,451 8 346 18,166 Buelton 1,082 1,807 841 1,559 82 1,381 4,814 30 4,599 4 300 16,499 Guadalupe 196 90 7,539 77 15 37 30 8,714 977 0 528 18,204 Unincorporated 67,224 34,874 24,291 17,446 7,924 6,446 4,593 972 97,699 3,968 2,347 267,784 Ventura County 23,327 1,689 169 22 12,068 8 5 0 3,975 - 3,633 44,895 San Luis Obispo County 820 999 33,076 1,701 48 346 300 528 2,342 2,339 - 42,498 Total 372,253 130,513 497,081 120,470 52,058 18,156 16,481 18,180 267,783 43,583 43,789 1,580,348 Santa Barbara Goleta Santa Maria Lompoc Carpinteria Solvang Buelton Guadalupe Unincorporate
d
Ventura
County
San Luis
Obispo County Total
Santa Barbara 519,024 271,529 385,221 69,577 109,726 25,687 44,178 13,423 575,056 743,388 66,812 2,823,621 Goleta 271,565 102,168 611,085 139,176 36,104 10,288 61,472 6,359 286,615 67,900 85,806 1,678,538 Santa Maria 396,109 612,784 1,208,068 55,484 15,831 34,039 25,426 72,914 804,933 16,524 601,101 3,843,214 Lompoc 70,355 139,240 55,358 141,057 3,749 13,503 28,133 2,123 181,217 1,808 74,274 710,818 Carpinteria 110,851 36,522 15,637 3,687 27,007 2,854 4,473 1,230 93,733 231,628 4,309 531,931 Solvang 25,940 10,304 33,954 13,530 2,888 6,222 5,714 1,561 67,028 523 18,895 186,559 Buelton 44,309 61,439 25,567 28,305 4,484 5,709 5,054 1,184 67,191 318 15,489 259,050 Guadalupe 13,705 6,450 73,048 2,126 1,256 1,577 1,182 9,728 19,227 7 4,580 132,887 Unincorporated 575,867 287,913 801,585 178,542 93,552 66,971 67,159 19,217 690,949 117,137 128,172 3,027,064 Ventura County 741,979 68,525 15,726 1,841 231,149 549 331 8 117,066 - 534,918 1,712,093 San Luis Obispo County 67,472 84,901 595,581 73,890 4,411 18,855 15,432 4,578 127,573 327,008 - 1,319,702 Total 2,837,177 1,681,775 3,820,832 707,214 530,158 186,255 258,555 132,324 3,030,589 1,506,242 1,534,357 16,225,477 Santa Barbara Goleta Santa Maria Lompoc Carpinteria Solvang Buelton Guadalupe Unincorporate
d
Ventura
County
San Luis
Obispo County Total
Santa Barbara 6,071 418 52 18 115 14 9 2 816 - - 7,516
Goleta 418 1,745 28 13 30 9 6 1 638 - - 2,888
Santa Maria 52 28 7,236 98 11 19 17 64 302 - - 7,828
Lompoc 18 13 98 1,471 4 11 11 4 187 - - 1,816
Carpinteria 115 30 11 4 777 2 1 0 156 - - 1,097
Solvang 14 9 19 11 2 269 36 1 140 - - 501
Buelton 9 6 17 11 1 36 192 1 108 - - 380
Guadalupe 2 1 64 4 0 1 1 132 12 - - 216
Unincorporated 816 638 302 187 156 140 108 12 2,072 - - 4,431 Ventura County - - - - - - - - - - - -
San Luis Obispo County - - - - - - - - - - - -
Total 7,516 2,888 7,828 1,816 1,097 501 380 216 4,431 - - 26,674 Santa Barbara Goleta Santa Maria Lompoc Carpinteria Solvang Buelton Guadalupe Unincorporate
d
Ventura
County
San Luis
Obispo County Total
Santa Barbara 9,513 3,232 3,200 974 1,466 467 333 147 6,490 - - 25,820 Goleta 3,178 2,942 1,742 581 644 274 199 79 3,748 - - 13,389 Santa Maria 3,204 1,738 18,062 2,218 814 644 539 606 6,740 - - 34,565 Lompoc 973 581 2,213 1,978 244 232 203 100 2,278 - - 8,802 Carpinteria 1,472 651 815 244 910 110 78 38 1,531 - - 5,849 Solvang 467 271 644 232 110 232 133 28 1,010 - - 3,127
Buelton 336 199 541 203 78 133 192 23 780 - - 2,485
Guadalupe 147 79 606 100 38 28 23 137 307 - - 1,465
Unincorporated 6,494 3,798 6,756 2,277 1,529 1,012 780 307 11,219 - - 34,172 Ventura County - - - - - - - - - - - -
San Luis Obispo County - - - - - - - - - - - -
Total 25,783 13,492 34,579 8,807 5,833 3,131 2,480 1,464 34,104 - - 129,673 CITIES
Daily Truck VMT
CITIES
Daily Auto VMT
Daily Truck Trips
CITIES
Daily Auto Trips
CITIES
Note that highlighted cells represent “through trips” within the City, thus trips not having an origin or destination within Santa Barbara ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix AQ-1
APPENDIX E – AIR QUALITY SUMMARIES
2020 Base Year
Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 42 42 42 42 42 42 42 42 42
Expressway 0 0 0 0 0 0 0 0 0
Principal Arterial 68 70 70 70 68 70 70 70 69
Minor Arterial 66 66 66 66 66 66 66 66 66
Urban Collector 136 136 136 136 136 136 136 136 136
Rural Collector 0 0 0 0 0 0 0 0 0
Local Roads 117 117 117 117 117 117 117 117 117
Ramps 10 9 9 9 10 9 9 9 9
System Ramp to Ramp Connector 0 0 0 0 0 0 0 0 0
HOV 0 0 0 0 0 0 0 0 0
Total 439 441 441 441 439 441 441 441 440
*Note: Travel statistics do not include travel on centroids or local roads w hich are not included in the model.

Units
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
VMT - Total 194,321 200,609 157,658 183,746 251,376 133,575 83,847 99,166 1,304,299 VHT - Total 4,047 4,073 3,203 3,793 5,606 2,665 1,675 1,896 26,958 VHD - Total 198 34 28 122 583 13 0 0 979
*Note: Travel statistics do not include travel on centroids or local roads w hich are not included in the model.

Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 99,288 94,683 74,004 90,238 121,756 67,703 41,255 59,128 648,055 Expressway 0 0 0 0 0 0 0 0 0
Principal Arterial 40,536 45,633 35,784 40,163 53,395 27,592 17,923 17,179 278,206 Minor Arterial 19,129 20,866 16,631 18,768 29,067 13,479 8,744 7,580 134,266 Urban Collector 24,469 27,420 21,865 24,277 34,196 16,986 10,795 9,989 169,998 Rural Collector 0 0 0 0 0 0 0 0 0
Local Roads 3,163 3,688 2,888 3,297 4,515 2,244 1,518 1,232 22,545 Ramps 7,735 8,319 6,486 7,002 8,448 5,571 3,611 4,057 51,229 System Ramp to Ramp Connector 0 0 0 0 0 0 0 0 0
HOV 0 0 0 0 0 0 0 0 0
Total 194,321 200,609 157,658 183,746 251,376 133,575 83,847 99,166 1,304,299 Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 1,666 1,485 1,162 1,436 2,230 1,061 647 927 10,614 Expressway 0 0 0 0 0 0 0 0 0
Principal Arterial 929 1,023 804 927 1,284 617 397 380 6,361 Minor Arterial 467 498 399 454 749 322 208 179 3,276
Urban Collector 594 658 526 585 859 408 258 239 4,128
Rural Collector 0 0 0 0 0 0 0 0 0
Local Roads 134 137 97 158 202 73 47 39 887
Ramps 256 273 214 232 282 184 118 132 1,692
System Ramp to Ramp Connector 0 0 0 0 0 0 0 0 0
HOV 0 0 0 0 0 0 0 0 0
Total 4,047 4,073 3,203 3,793 5,606 2,665 1,675 1,896 26,958 Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 113 1 4 23 324 2 0 0 466
Expressway 0 0 0 0 0 0 0 0 0
Principal Arterial 24 5 8 28 91 4 0 0 159
Minor Arterial 14 3 5 8 60 2 0 0 92
Urban Collector 9 2 3 4 41 2 0 0 61
Rural Collector 0 0 0 0 0 0 0 0 0
Local Roads 36 22 7 55 62 3 0 0 184
Ramps 4 1 1 3 5 1 0 0 15
System Ramp to Ramp Connector 0 0 0 0 0 0 0 0 0
HOV 0 0 0 0 0 0 0 0 0
Total 198 34 28 122 583 13 0 0 979
Model Update and EIR SBCITY-2020Base_FINAL
Total Vehicle Hours of Delay (VHD) by Facility Classification Model Update and EIR SBCITY-2020Base_FINAL
Total Vehicle Hours Traveled (VHT) by Facility Classification Model Update and EIR SBCITY-2020Base_FINAL
Total Vehicle Miles Traveled (VMT) by Facility Classification Model Update and EIR SBCITY-2020Base_FINAL
Total Vehicle Travel
Model Update and EIR SBCITY-2020Base_FINAL
Lane Miles of Travel by Functional Classification
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix AQ-2
Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 59.6 63.8 63.7 62.8 54.6 63.8 63.8 63.8 0.0
Expressway
Principal Arterial 43.6 44.6 44.5 43.3 41.6 44.7 45.2 45.2 0.0 Minor Arterial 41.0 41.9 41.6 41.3 38.8 41.8 42.1 42.5 0.0 Urban Collector 41.2 41.7 41.5 41.5 39.8 41.6 41.8 41.8 0.0 Rural Collector
Ramp 23.6 26.9 29.8 20.9 22.3 30.7 32.0 31.9 0.0
Local Road #REF! #REF! #REF! #REF! #REF! #REF! #REF! #REF! #REF! System Ramp to Ramp Connector
HOV
Total 48.0 49.3 49.2 48.4 44.8 50.1 50.1 52.3 0.0
Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway and HOV 59.6 63.8 63.7 62.8 54.6 63.8 63.8 63.8 61.1 Arterials 42.7 43.7 43.6 42.7 40.6 43.7 44.1 44.3 42.8
Collectors 41.2 41.7 41.5 41.5 39.8 41.6 41.8 41.8 41.2 Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 51.1% 47.2% 46.9% 49.1% 48.4% 50.7% 49.2% 59.6% 49.7% Expressway 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Principal Arterial 20.9% 22.7% 22.7% 21.9% 21.2% 20.7% 21.4% 17.3% 21.3% Minor Arterial 9.8% 10.4% 10.5% 10.2% 11.6% 10.1% 10.4% 7.6% 10.3% Urban Collector 12.6% 13.7% 13.9% 13.2% 13.6% 12.7% 12.9% 10.1% 13.0% Rural Collector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Ramp 1.6% 1.8% 1.8% 1.8% 1.8% 1.7% 1.8% 1.2% 1.7%
Local Road 4.0% 4.1% 4.1% 3.8% 3.4% 4.2% 4.3% 4.1% 3.9% System Ramp to Ramp Connector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% HOV 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 41.2% 36.5% 36.3% 37.9% 39.8% 39.8% 38.6% 48.9% 39.4% Expressway 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Principal Arterial 23.0% 25.1% 25.1% 24.4% 22.9% 23.1% 23.7% 20.1% 23.6% Minor Arterial 11.5% 12.2% 12.5% 12.0% 13.4% 12.1% 12.4% 9.4% 12.2% Urban Collector 14.7% 16.2% 16.4% 15.4% 15.3% 15.3% 15.4% 12.6% 15.3% Rural Collector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Ramp 3.3% 3.4% 3.0% 4.2% 3.6% 2.7% 2.8% 2.0% 3.3%
Local Road 6.3% 6.7% 6.7% 6.1% 5.0% 6.9% 7.1% 7.0% 6.3% System Ramp to Ramp Connector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% HOV 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 56.8% 3.1% 13.5% 18.6% 55.6% 12.6% 0.4% 5.7% 47.6% Expressway 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Principal Arterial 11.9% 15.2% 28.3% 23.3% 15.5% 27.9% 21.2% 18.6% 16.3% Minor Arterial 6.9% 8.6% 17.4% 6.9% 10.3% 16.6% 30.2% 10.7% 9.4% Urban Collector 4.5% 6.6% 12.2% 3.6% 7.0% 11.7% 14.7% 15.4% 6.3% Rural Collector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Ramp 18.0% 63.7% 23.6% 45.3% 10.6% 23.1% 23.9% 42.2% 18.8% Local Road 2.0% 2.7% 4.9% 2.3% 0.9% 8.1% 9.7% 7.4% 1.6% System Ramp to Ramp Connector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% HOV 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Model Update and EIR SBCITY-2020Base_FINAL
Percent of Total Vehicle Hours of Delay (VHD) by Facility Classification Percent of Total Vehicle Hours Traveled (VHT) by Facility Classification Model Update and EIR SBCITY-2020Base_FINAL
Percent of Vehicle Miles Traveled (VMT) by Facility Classification Model Update and EIR SBCITY-2020Base_FINAL
Average Speeds (MPH) by Functional Classification (Autos and Trucks Combined) Model Update and EIR SBCITY-2020Base_FINAL
Average Speeds (MPH) by Functional Classification (Autos and Trucks Combined) Model Update and EIR SBCITY-2020Base_FINAL
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix AQ-3
Speed Ranges
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily Percent of Travel
0 - 5 mph 0 0 0 282 0 0 0 0 282 0.0%
5 - 10 mph 191 250 0 0 308 0 0 0 748 0.1%
10 - 15 mph 86 0 0 0 544 0 0 0 630 0.0%
15 - 20 mph 161 0 128 74 306 0 0 0 669 0.1%
20 - 25 mph 248 97 320 282 156 120 0 0 1,222 0.1%
25 - 30 mph 7,600 8,331 6,435 8,464 11,499 5,615 3,334 3,729 55,008 4.2% 30 - 35 mph 5,987 5,784 4,415 4,378 13,263 3,421 2,278 1,635 41,162 3.2% 35 - 40 mph 7,630 6,108 5,135 7,057 29,987 3,927 2,284 1,913 64,041 4.9% 40 - 45 mph 70,531 82,192 65,059 70,641 88,411 50,748 31,378 29,545 488,504 37.5% 45 - 50 mph 9,587 1,160 579 530 11,783 711 2,404 2,454 29,209 2.2% 50 - 55 mph 9,734 1,951 1,538 1,750 9,227 1,296 892 739 27,127 2.1% 55 - 60 mph 27,432 21,052 15,701 21,598 34,506 14,094 9,361 13,340 157,083 12.0% 60 - 65 mph 55,134 73,685 58,348 68,690 51,386 53,643 20,781 40,158 421,826 32.3% 65 - 70 mph 0 0 0 0 0 0 11,135 5,652 16,787 1.3%
70 - 75 mph 0 0 0 0 0 0 0 0 0 0.0%
75 - 80 mph 0 0 0 0 0 0 0 0 0 0.0%
VMT - Total 194,321 200,609 157,658 183,746 251,376 133,575 83,847 99,166 1,304,299 100.0% *Note: Travel statistics do not include travel on centroids or local roads w hich are not included in the model.

Units
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
VMT - Total 227,388 240,573 187,532 220,376 295,282 161,593 106,051 122,504 1,561,299 VHT - Total 4,859 4,919 3,838 4,676 7,087 3,232 2,108 2,336 33,055 VHD - Total 328 78 60 271 1,134 32 1 1 1,906
*Note: Travel statistics do not include travel on centroids or local roads w hich are not included in the model.

Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 112,745 114,772 88,583 108,296 137,276 83,238 54,018 74,535 773,465 Expressway 0 0 0 0 0 0 0 0 0
Principal Arterial 48,323 52,802 40,950 47,026 63,906 31,950 21,578 19,976 326,511 Minor Arterial 23,638 25,288 20,176 23,136 36,235 16,418 10,586 9,222 164,700 Urban Collector 29,831 33,196 26,555 29,686 42,036 20,640 13,335 12,255 207,535 Rural Collector 0 0 0 0 0 0 0 0 0
Local Roads 3,793 4,400 3,431 3,957 6,471 2,660 1,802 1,447 27,962 Ramps 9,058 10,115 7,836 8,273 9,358 6,687 4,731 5,067 61,127 System Ramp to Ramp Connector 0 0 0 0 0 0 0 0 0
HOV 2,887 2,425 1,968 2,254 2,988 869 614 633 14,638
Total 230,274 242,998 189,499 222,630 298,271 162,462 106,665 123,137 1,575,936 Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 1,958 1,806 1,403 1,790 2,808 1,313 849 1,171 13,098 Expressway 0 0 0 0 0 0 0 0 0
Principal Arterial 1,119 1,189 925 1,128 1,616 716 478 442 7,613 Minor Arterial 588 608 491 569 986 396 251 217 4,106
Urban Collector 733 798 640 720 1,082 497 319 293 5,082 Rural Collector 0 0 0 0 0 0 0 0 0
Local Roads 159 183 117 191 279 88 56 45 1,119
Ramps 303 335 261 278 316 222 155 166 2,037
System Ramp to Ramp Connector 0 0 0 0 0 0 0 0 0
HOV 47 39 32 37 49 14 10 10 238
Total 4,907 4,958 3,870 4,713 7,136 3,246 2,118 2,346 33,293 Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 192 5 15 92 657 9 0 0 970
Expressway 0 0 0 0 0 0 0 0 0
Principal Arterial 40 11 14 75 187 6 0 0 335
Minor Arterial 28 9 12 20 127 6 0 0 202
Urban Collector 19 4 5 10 76 3 0 0 118
Rural Collector 0 0 0 0 0 0 0 0 0
Local Roads 41 46 10 68 77 5 0 0 247
Ramps 8 4 4 7 9 2 0 0 35
System Ramp to Ramp Connector 0 0 0 0 0 0 0 0 0
HOV 0 0 0 0 0 0 0 0 0
Total 328 78 60 271 1,134 32 1 1 1,906
Model Update and EIR SBCITY-2035BaselineGP_V2
Total Vehicle Hours Traveled (VHT) by Facility Classification Model Update and EIR SBCITY-2035BaselineGP_V2
Total Vehicle Miles Traveled (VMT) by Facility Classification Model Update and EIR SBCITY-2035BaselineGP_V2
Total Vehicle Travel
Model Update and EIR SBCITY-2035BaselineGP_V2
Lane Miles of Travel by Functional Classification
Model Update and EIR SBCITY-2035BaselineGP_V2
Total Vehicle Hours of Delay (VHD) by Facility Classification ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix AQ-5
Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 57.6 63.6 63.1 60.5 48.9 63.4 63.6 63.7 0.0
Expressway
Principal Arterial 43.2 44.4 44.3 41.7 39.5 44.6 45.1 45.1 0.0 Minor Arterial 40.2 41.6 41.1 40.7 36.7 41.4 42.2 42.4 0.0 Urban Collector 40.7 41.6 41.5 41.2 38.8 41.6 41.8 41.8 0.0 Rural Collector
Ramp 23.8 24.0 29.3 20.7 23.2 30.2 31.9 31.8 0.0
Local Road #REF! #REF! #REF! #REF! #REF! #REF! #REF! #REF! #REF! System Ramp to Ramp Connector
HOV 29.9 30.2 30.0 29.7 29.6 30.1 30.5 30.6 0.0
Total 46.8 48.9 48.9 47.1 41.7 50.0 50.3 52.5 0.0
Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway and HOV 57.7 63.5 63.1 60.5 49.1 63.4 63.6 63.6 59.1 Arterials 42.2 43.5 43.2 41.4 38.5 43.5 44.1 44.2 41.9
Collectors 40.7 41.6 41.5 41.2 38.8 41.6 41.8 41.8 40.8 Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 49.0% 47.2% 46.7% 48.6% 46.0% 51.2% 50.6% 60.5% 49.1% Expressway 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Principal Arterial 21.0% 21.7% 21.6% 21.1% 21.4% 19.7% 20.2% 16.2% 20.7% Minor Arterial 10.3% 10.4% 10.6% 10.4% 12.1% 10.1% 9.9% 7.5% 10.5% Urban Collector 13.0% 13.7% 14.0% 13.3% 14.1% 12.7% 12.5% 10.0% 13.2% Rural Collector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Ramp 1.6% 1.8% 1.8% 1.8% 2.2% 1.6% 1.7% 1.2% 1.8%
Local Road 3.9% 4.2% 4.1% 3.7% 3.1% 4.1% 4.4% 4.1% 3.9% System Ramp to Ramp Connector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% HOV 1.3% 1.0% 1.0% 1.0% 1.0% 0.5% 0.6% 0.5% 0.9%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 39.9% 36.4% 36.3% 38.0% 39.4% 40.5% 40.1% 49.9% 39.3% Expressway 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Principal Arterial 22.8% 24.0% 23.9% 23.9% 22.6% 22.1% 22.6% 18.9% 22.9% Minor Arterial 12.0% 12.3% 12.7% 12.1% 13.8% 12.2% 11.9% 9.3% 12.3% Urban Collector 14.9% 16.1% 16.5% 15.3% 15.2% 15.3% 15.1% 12.5% 15.3% Rural Collector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Ramp 3.2% 3.7% 3.0% 4.1% 3.9% 2.7% 2.7% 1.9% 3.4%
Local Road 6.2% 6.8% 6.8% 5.9% 4.4% 6.8% 7.3% 7.1% 6.1% System Ramp to Ramp Connector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% HOV 1.0% 0.8% 0.8% 0.8% 0.7% 0.4% 0.5% 0.4% 0.7%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily
Freeway 58.6% 6.4% 24.2% 33.9% 58.0% 28.0% 0.6% 21.8% 50.9% Expressway 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Principal Arterial 12.3% 14.1% 23.7% 27.8% 16.5% 19.3% 34.6% 12.2% 17.6% Minor Arterial 8.4% 11.1% 20.1% 7.2% 11.2% 20.4% 28.9% 11.3% 10.6% Urban Collector 5.8% 5.3% 8.4% 3.6% 6.7% 8.9% 13.0% 23.2% 6.2% Rural Collector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Ramp 12.6% 58.5% 16.1% 25.0% 6.8% 15.7% 9.9% 23.4% 13.0% Local Road 2.3% 4.7% 7.5% 2.5% 0.8% 7.7% 13.0% 8.0% 1.8% System Ramp to Ramp Connector 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% HOV 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Model Update and EIR SBCITY-2035BaselineGP_V2
Percent of Vehicle Miles Traveled (VMT) by Facility Classification Model Update and EIR SBCITY-2035BaselineGP_V2
Average Speeds (MPH) by Functional Classification (Autos and Trucks Combined) Model Update and EIR SBCITY-2035BaselineGP_V2
Average Speeds (MPH) by Functional Classification (Autos and Trucks Combined) Model Update and EIR SBCITY-2035BaselineGP_V2
Model Update and EIR SBCITY-2035BaselineGP_V2
Percent of Total Vehicle Hours of Delay (VHD) by Facility Classification Percent of Total Vehicle Hours Traveled (VHT) by Facility Classification ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix AQ-6
Speed Ranges
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily Percent of Travel
0 - 5 mph 0 0 0 292 289 0 0 0 581 0.0%
5 - 10 mph 194 285 0 0 39 0 0 0 517 0.0%
10 - 15 mph 178 0 0 0 613 0 0 0 791 0.1%
15 - 20 mph 103 0 177 2,176 2,320 127 0 0 4,904 0.3%
20 - 25 mph 687 161 409 360 2,627 262 0 0 4,506 0.3%
25 - 30 mph 9,494 10,430 7,906 8,300 16,534 6,785 4,500 4,746 68,694 4.4% 30 - 35 mph 7,167 6,648 4,943 5,229 23,193 3,852 2,415 1,835 55,281 3.5% 35 - 40 mph 13,691 8,093 8,080 9,881 54,224 5,326 3,066 2,669 105,030 6.7% 40 - 45 mph 89,467 96,773 75,128 83,309 85,844 59,941 38,665 35,636 564,762 35.8% 45 - 50 mph 3,034 1,051 461 2,608 24,099 551 2,317 2,215 36,336 2.3% 50 - 55 mph 16,133 2,284 1,782 5,707 18,284 1,468 1,038 841 47,537 3.0% 55 - 60 mph 38,645 29,062 21,642 43,895 32,325 18,949 13,969 18,970 217,456 13.8% 60 - 65 mph 51,482 88,213 68,971 60,874 37,880 65,202 31,571 51,953 456,145 28.9% 65 - 70 mph 0 0 0 0 0 0 9,123 4,273 13,396 0.9%
70 - 75 mph 0 0 0 0 0 0 0 0 0 0.0%
75 - 80 mph 0 0 0 0 0 0 0 0 0 0.0%
VMT - Total 230,274 242,998 189,499 222,630 298,271 162,462 106,665 123,137 1,575,936 100.0% *Note: Travel statistics do not include travel on centroids or local roads w hich are not included in the model.

Functional Classification
AM
7 AM to 9 AM
Late AM
9 AM to 12 PM
Lunch
12 PM to 2 PM
Early PM
2 PM to 4 PM
PM
4 PM to 6 PM
Evening
6 PM to 8 PM
Late Evening
8 PM to 12 AM
Night
12 AM to 7 AM Average Daily Percent of Travel
Freeway 112,745 114,772 88,583 108,296 137,276 83,238 54,018 74,535 773,465 49.1% Expressway 0 0 0 0 0 0 0 0 0 0.0%
Principal Arterial 48,323 52,802 40,950 47,026 63,906 31,950 21,578 19,976 326,511 20.7% Minor Arterial 23,638 25,288 20,176 23,136 36,235 16,418 10,586 9,222 164,700 10.5% Urban Collector 29,831 33,196 26,555 29,686 42,036 20,640 13,335 12,255 207,535 13.2% Rural Collector 0 0 0 0 0 0 0 0 0 0.0%
Local Roads 3,793 4,400 3,431 3,957 6,471 2,660 1,802 1,447 27,962 1.8% Ramps 9,058 10,115 7,836 8,273 9,358 6,687 4,731 5,067 61,127 3.9% System Ramp to Ramp Connector 0 0 0 0 0 0 0 0 0 0.0%
HOV 2,887 2,425 1,968 2,254 2,988 869 614 633 14,638 0.9% Total 230,274 242,998 189,499 222,630 298,271 162,462 106,665 123,137 1,575,936 100.0% Air Quality - City of Santa Barbara
Model Update and EIR SBCITY-2035BaselineGP_V2
Total Vehicle Miles Traveled (VMT) by Facility Classification Model Update and EIR SBCITY-2035BaselineGP_V2
Vehicle Miles Traveled
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix VMT-1
APPENDIX F – SB 743 VMT SUMMARIES
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix VMT-2
2020 Base Year
2035 Forecast Year
ID Purpose Productions Attractions
1 Home-based Work 239,158 1,627,664
2 Home-based School 132,782 11,119
3 Home-based Unincorporated 423,836 771,389
4 Home-based Shopping 100,225 161,613
5 Non-Home-based Work 129,442 236,589
6 Non-Home-based Unincorporated 121,966 611,959
7 Visitor 54,384 364,505
1,201,792 3,784,838
Total Home-based VMT/Capita 10.43
Total Work-based VMT/Employee 24.32
Total Employees 76,654
Total Home-based VMT 896,000
Total Work-based VMT 1,864,254
Total Population 85,942
Total VMT/Service Population 30.67
Total VMT
Santa Barbara
ID Purpose Productions Attractions
1 Home-based Work 314,437 1,863,569
2 Home-based School 195,044 10,910
3 Home-based Unincorporated 558,841 861,197
4 Home-based Shopping 134,185 173,507
5 Non-Home-based Work 199,490 191,942
6 Non-Home-based Unincorporated 172,499 597,097
7 Visitor 55,548 822,547
1,630,044 4,520,769
Total Home-based VMT/Capita 10.90
Total Work-based VMT/Employee 24.97
Total Employees 82,335
Total Home-based VMT 1,202,507
Total Work-based VMT 2,055,512
Total Population 110,340
Total VMT/Service Population 31.92
Total VMT
Santa Barbara
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-1
APPENDIX G– MODEL VALIDATION
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-2
Functional Classification ADT Counts
ADT
Volumes
ADT %
(Count /
Volume)
Freeway 1474150 1454967 -1.3%
Expressway 0 0
Principal Arterial 1698820 1876170 9.5%
Minor Arterial 868850 787370 -10.3%
Collector 322500 213200 -51.3%
Local Roads 39130 26742 -46.3%
Ramps 267425 274551 2.6%
4670875 4633001 -0.8%
Number of Counts ADT
Freeways & HOV 25
Major Arterials 97
Minor Arterials 67
Collectors 42
Ramps 37
Total Number of Counts 268
ADT
Caltrans and FHWA Criteria:

Freeways +/- 7% -1%
Major Arterials +/- 10% 10%
Minor Arterials +/- 15% -10%
ADT
Caltrans Recommended Guidance:
(<40=Green, 40-45=Yellow, >45=Red)
%RMSE = 32
FHWA Recommended Guidance:
(Periods: <40=Green, 40-45=Yellow, >45=Red)
(Day: <30=Green, 30-35=Yellow, >35=Red)
%RMSE = 32
FHWA Criteria: ADT
Coefficient of Determination (R2
)
(>=0.88=Green, <0.88=Red) 0.89
Caltrans Criteria: ADT
% of Links within Caltrans Standard Deviations
(>=0.75=Green, 0.70-0.75=Yellow, <0.70=Red) 92%
*Note: Caltrans Criteria is from the Caltrans Travel Forecasting Guidelines, November 1992.
*Note: Collectors are the lowest functional classified facilities in the model network and the model is not validated for counts on collectors. *Note: Travel statistics do not include travel on centroids. *Note: Caltrans Criteria is from the Caltrans Travel Forecasting Guidelines, November 1992.
*Note: FHWA Criteria was obtained from the Model Validation and Reasonableness Checking Manual
Model Update and EIR Count - Volume Comparison - (SBCITY-2020Base_FINAL) - date and EIR Count - Volume Comparison - (SBCITY-2020Base date and EIR Count - Volume Comparison - (SBCITY-2020Base date and EIR Count - Volume Comparison - (SBCITY-2020Base date and EIR Count - Volume Comparison - (SBCITY-2020Base date and EIR Count - Volume Comparison - (SBCITY-2020Base *Note: Caltrans Criteria is from the Caltrans Travel Forecasting Guidelines, November 1992.
*Note: FHWA Criteria was obtained from the Model Validation and Reasonableness Checking Manual
*Note: The coefficient of determination (R 2 ) of 0.88 is sourced in both Caltrans and FHWA criteria.
*Note: Caltrans Criteria is from the Caltrans Travel Forecasting Guidelines, November 1992.
*Note: FHWA Criteria was obtained from the Model Validation and Reasonableness Checking Manual
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-3
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-4
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily
Screenline Name Number
Screenline
Direction
Number of
Counts
Total Sum of
Counts
Total
Volumes at
Locations
with Counts
Percent
Deviation
FHWA
Pass/Fail
Criteria
East of US-101 bewteen Las Positas Rd and Haley St 1 NS 6 127,730 135,179 5.8% PASS West of US-101 bewteen Las Positas Rd and Haley St 2 NS 4 63,080 45,847 -27.3% PASS South of State St between US-101 and Alamar Ave 3 EW 5 53,200 45,491 -14.5% PASS East of Milpas St between Arbolado Rd and Cabrillo Blvd 4 NS 5 22,535 13,236 -41.3% PASS East of Salinas St between Cabrillo Blvd and Mountain Rd 5 NS 4 57,030 53,018 -7.0% PASS East of State St between Constance Ave and Shoreline Dr 6 NS 6 140,310 129,039 -8.0% PASS North of Anapamu Street between Gillespie St and Milpas St 7 EW 9 88,000 74,115 -15.8% PASS Between Modoc Rd and State St 8 EW 1 16,270 16,935 4.1% PASS North of Shoreline Dr between Loma Alta Dr and Channel Dr 9 NS 2 15,410 13,755 -10.7% PASS North of Gutierrez St between Bath St and Alameda Padre Serra 10 EW 3 40,200 35,503 -11.7% PASS West of De La Vina St between Vernon Rd and Shoreline Dr 11 NS 3 48,120 58,109 20.8% PASS North of Mission St between Modoc Rd and Alameda Padre Serra 12 EW 6 47,520 48,401 1.9% PASS Cutline on Cliff Drive West of Meigs Rd 13 NS 1 17,190 10,648 -38.1% PASS Cutline on US-101 (Western Santa Barbara City Border) 101 NS 4 146,000 137,845 -5.6% PASS Cutline on SR-154 (Northern Santa Barbara City Border) 102 EW 3 24,500 20,507 -16.3% PASS Cutline on PCH and SR-192 (Eastern Santa Barbara City Border) 103 EW 2 70,100 83,058 18.5% PASS Combined Screenline Totals 64 977,195 920,686 -5.8%
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-5
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily NS
Screenline 1 East of US-101 bewteen Las Positas Rd and Haley St Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID S LA CUMBRE RD 10641 134400 10641 18040 9020 9020 3187_AB 3187_BA S HOPE AVE 9517 134400 9517 8860 4430 4430 3178_AB 3178_BA HITCHCOCK WAY 3653 63000
APPLE GROVE LN 3060 63000
PEACH GROVE LN 0 63000
STATE ST 36382 168000 36382 20860 10430 10430 3108_AB 3108_BA W ARRELLAGA ST 9024 63000
LAS POSITAS RD 8984 134400 8984 22010 11005 11005 3074_AB 3074_BA TREASURE DR 2551 54600
W JUNIPERO ST 4108 63000
W LOS OLIVOS ST 322 63000
W PADRE ST 0 54600
OAK PARK LN 0 54600
W PUEBLO ST 2463 63000
W MISSION ST 30497 168000 30497 23840 11920 11920 3125_AB 3125_BA W SOLA ST 581 63000
W MICHELTORENA ST 6510 63000
W ANAPAMU ST 0 63000
W CARRILLO ST 39157 252000 39157 34120 17060 17060 3093_BA 3093_AB Totals 167453 1785000 135179 127730 6 6 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-6
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily NS
Screenline 2 West of US-101 bewteen Las Positas Rd and Haley St Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID AMTRAK 0 0
MODOC RD 6464 67200
LAS PALMAS DR 10096 67200
CALLE DE LOS AMIGOS 212 54600
VERONICA SPRINGS RD 5746 54600
PALERMO DR 237 54600
FERRARA WAY 0 63000
MODOC RD 7821 67200 7821 10370 5185 5185 3145_AB 3145_BA MODOC RD 6375 67200
LAS POSITAS RD 21988 168000
W MISSION ST 9735 63000 9735 11870 5935 5935 3113_AB 3113_BA W CANON PERDIDO ST 0 63000
W MICHELTORENA ST 6518 63000 6518 9650 4825 4825 2017_BA 2017_AB W ANAPAMU ST 0 63000
W DE LA GUERRA ST 0 54600
W CARRILLO ST 21773 134400 21773 31190 15595 15595 2005_BA 2005_AB Totals 96966 1104600 45847 63080 4 4 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-7
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily EW
Screenline 3 South of State St between US-101 and Alamar Ave Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID LA CUMBRE PLAZA 28 109200
PLAZA AVE 0 109200
13062 151200 13062 10150 10150 0 3107_AB 3107_BA
S LA CUMBRE RD 14791 134400 14791 11760 5880 5880 3175_BA 3175_AB S HOPE AVE 7696 134400 7696 7750 3875 3875 3171_BA 3171_AB HITCHCOCK WAY 5191 63000 5191 7540 3770 3770 3167_AB 3167_BA S ONTARE RD 5034 54600
W CALLE LAURELES 660 54600
STATE ST 13534 134400
10800 168000
LAS POSITAS RD 4751 134400 4751 16000 8000 8000 3163_BA 3163_AB MIRADERO DR 0 54600
ALAMAR AVE 3687 63000
Totals 79234 1365000 45491 53200 5 5 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-8
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily NS
Screenline 4 East of Milpas St between Arbolado Rd and Cabrillo Blvd Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID AMTRAK 0 0
BIKEWAY 0 0
E HALEY ST 0 31500 0 2850 2850 0 3033_BA 3033_AB
E MASON ST 2930 63000
E CANON PERDIDO ST 0 54600
E ORTEGA ST 23 54600
E DE LA GUERRA ST 3754 63000
E COTA ST 3009 63000
GARCIA RD 3054 63000
US HWY 101 SB 40636 239400
US HWY 101 NB 44889 239400
3756 75600
QUINIENTOS ST 1480 54600 480 4800 2400 2400 3025_AB 3025_BA CARPINTERIA ST 5530 63000 5530 6150 3075 3075 3021_BA 3021_AB CALLE PUERTO VALLART 0 54600
CACIQUE ST 6063 63000
4518 86400 4518 4155 4155 3014_AB
E CABRILLO BLVD 15002 168000
E YANONALI ST 0 54600
E GUTIERREZ ST 1707 63000 1707 4580 2290 2290 3029_BA 3029_AB E MONTECITO ST 8666 63000
Totals 145018 1617300 12236 22535 5 4 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-9
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily NS
Screenline 5 East of Salinas St between Cabrillo Blvd and Mountain Rd Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID AMTRAK 0 0
BIKEWAY 0 0
LAS ALTURAS RD 247 63000
SYCAMORE CANYON RD 1572 63000 1572 3600 1800 1800 1100_BA 1100_AB LAS ALTURAS RD 247 63000
ALAMEDA PADRE SERRA 6847 63000
EUCALYPTUS HILL RD 1518 63000
HILLCREST RD 0 54600
MOUNTAIN DR 1329 67200 1329 3500 1750 1750 1305_BA 1305_AB MISSION RIDGE RD 1268 63000
US HWY 101 NB 47131 239400 47131 41950 41950 1002_AB
US HWY 101 SB 44942 171000
LOS PATOS WAY 212 75600
OLD COAST HWY 2986 63000 2986 7980 3990 3990 3007_BA 3007_AB Totals 108301 1048800 53018 57030 4 3 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-10
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily NS
Screenline 6 East of State St between Constance Ave and Shoreline Dr Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID AMTRAK 0 0
E CARRILLO ST 19513 151200 19513 13470 6735 6735 2003_BA 2003_AB E FIGUEROA ST 5995 58800
E JUNIPERO ST 0 54600
E CONSTANCE AVE 9091 63000 9091 8080 4040 4040 3017_AB 3017_BA E PUEBLO ST 232 54600
E QUINTO ST 56 54600
E MISSION ST 11983 84000 11983 8310 4155 4155 3136_BA 3136_AB E ISLAY ST 140 54600
W PEDREGOSA ST 1536 63000
E VALERIO ST 1962 63000
E LOS OLIVOS ST 3882 63000
E PADRE ST 11 54600
E VICTORIA ST 2287 58800
E MICHELTORENA ST 2855 58800 2855 7450 3725 3725 2016_AB 2016_BA E SOLA ST 2020 58800
E ARRELLAGA ST 1377 58800
E ANAPAMU ST 2420 63000
US HWY 101 NB 44938 239400 44938 51500 51500 1004_AB
US HWY 101 SB 40660 239400 40660 51500 51500 1004_BA
E COTA ST 4030 58800
E ORTEGA ST 2987 58800
E CANON PERDIDO ST 2201 58800
E DE LA GUERRA ST 4676 58800
E HALEY ST 10446 75600
E GUTIERREZ ST 8174 140400
E YANONALI ST 5686 63000
E MASON ST 110 54600
E CABRILLO BLVD 9992 168000
E MONTECITO ST 2104 58800
Totals 201362 2391600 129039 140310 6 4 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-11
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily EW
Screenline 7 North of Anapamu Street between Gillespie St and Milpas St Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID AMTRAK 0 0
US HWY 101 SB 45276 239400
US HWY 101 NB 48146 239400
SAN ANDRES ST 11084 63000 11084 8280 4140 4140 3102_BA 3102_AB BATH ST 2884 31500 2884 6270 0 6270 3086_AB 3086_BA
15577 151200 15577 14420 14420 0 3098_AB 3098_BA
13531 152400 13531 11440 0 11440 3094_AB 3094_BA
CASTILLO ST 3219 31500 3219 5840 5820 20 3090_AB 3090_BA CHAPALA ST 10632 113400
DE LA VINA ST 3894 63000 3894 10220 10210 10 3082_AB 3082_BA STATE ST 6359 63000
ANACAPA ST 12191 75600 12191 12130 12130 2001_AB
ALTA VISTA RD 297 54600
N NOPAL ST 0 54600
LAGUNA ST 8974 63000
OLIVE ST 5268 63000
GARDEN ST 3138 58800
SANTA BARBARA ST 2391 63000 2391 9200 9200 2024_AB
N MILPAS ST 9343 84000 9343 10200 5100 5100 2021_AB 2021_BA Totals 202206 1664400 74115 88000 9 7 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-12
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily EW
Screenline 8 Between Modoc Rd and State St
Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID SERENA RD 0 54600
CLINTON TER 1037 54600
LAS POSITAS RD 6727 134400
STANLEY DR 1533 109200
DE LA VINA ST 10567 168000
STATE ST 16935 134400 16935 16270 8135 8135 3156_AB 3156_BA W ALAMAR AVE 3914 63000
Totals 40713 718200 16935 16270 1 1 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-13
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily NS
Screenline 9 North of Shoreline Dr between Loma Alta Dr and Channel Dr Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID POR LA MAR DR 0 54600
NINOS DR 0 54600
BATH ST 872 63000
GARDEN ST 1690 67200
GARDEN ST 1184 67200
CASTILLO ST 7241 168000 7241 6230 3115 3115 3063_BA 3063_AB BIKEWAY 0 0
STATE ST 3020 134400
ANACAPA ST 852 63000
CHAPALA ST 2649 63000
S CALLE CESAR CHAVEZ 3135 63000
S MILPAS ST 6514 84000 6514 9180 4590 4590 3013_AB 3013_BA CALLE PUERTO VALLART 0 54600
Totals 27157 936600 13755 15410 2 2 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-14
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily EW
Screenline 10 North of Gutierrez St between Bath St and Alameda Padre Serra Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID N SALSIPUEDES ST 1305 63000
N MILPAS ST 13747 168000 13747 19420 9710 9710 3032_AB 3032_BA DE LA VINA ST 722 67200
BATH ST 3869 31500 3869 4980 2490 2490 3071_AB 3071_BA
STATE ST 4984 63000
CHAPALA ST 10699 145800
ANACAPA ST 2708 140400
SANTA BARBARA ST 5040 126000
OLIVE ST 4222 63000
LAGUNA ST 7553 63000
GARDEN ST 17888 126000 17888 15800 7900 7900 3036_AB 3036_BA N NOPAL ST 1 54600
N QUARANTINA ST 2973 63000
ALAMEDA PADRE SERRA 5295 67200
N ALISOS ST 3 54600
N SOLEDAD ST 69 54600
N VOLUNTARIO ST 128 54600
N CANADA ST 0 54600
Totals 81205 1460100 35503 40200 3 3 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-15
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily NS
Screenline 11 West of De La Vina St between Vernon Rd and Shoreline Dr Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID AMTRAK 0 0
US HWY 101 NB 41349 239400
US HWY 101 SB 37596 239400
W SOLA ST 1565 58800
W PUEBLO ST 2149 63000
W LOS OLIVOS ST 1992 63000
W JUNIPERO ST 1444 63000
W ALAMAR AVE 1377 54600
W QUINTO ST 877 54600
W CONSTANCE AV 1159 54600
W FIGUEROA ST 924 58800
W ANAPAMU ST 725 58800
W VICTORIA ST 1169 58800
W CANON PERDIDO ST 1422 58800
W DE LA GUERRA ST 1049 58800
W CARRILLO ST 35342 226800 35342 26730 13365 13365 3085_BA 3085_AB W ISLAY ST 288 54600
W PEDREGOSA ST 1090 63000
W MISSION ST 19702 168000 19702 18160 9080 9080 3133_AB 3133_BA W ARRELLAGA ST 6522 58800
W VALERIO ST 2546 63000
W MICHELTORENA ST 3959 58800
W HALEY ST 14698 84000
W COTA ST 697 63000
W MASON ST 153 54600
NATOMA AV 0 54600
3589 75600
3064 75600 3064 3230 0 3230 3061_AB 3061_BA
COTTAGE GROVE AV 92 50400
W MONTECITO ST 4491 58800
LOS AGUAJES AVE 5011 63000
W YANONALI ST 0 54600
W ORTEGA ST 964 63000
W CABRILLO BLVD 7111 168000
Totals 204117 2742600 58109 48120 3 3 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-16
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily EW
Screenline 12 North of Mission St between Modoc Rd and Alameda Padre Serra Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID AMTRAK 0 0
US HWY 101 SB 56000 239400
US HWY 101 NB 65417 239400
DE LA VINA ST 7004 84000 7004 9600 9600 0 3130_AB 3130_BA MOUNTAIN AVE 1985 63000
GILLESPIE ST 2035 63000
6509 75600
OAK AV 0 54600
MODOC RD 7897 67200 7897 8280 4140 4140 3110_AB 3110_BA CASTILLO ST 8373 63000 8373 4010 2005 2005 3122_AB 3122_BA 14565 151200 14565 12350 12350 0 3114_AB 3114_BA
BATH ST 2937 63000 2937 4800 2400 2400 3126_AB 3126_BA
STATE ST 7625 67200 7625 8480 4240 4240 3134_BA 3134_AB CHAPALA ST 6054 62400
ANACAPA ST 3062 134400
E MISSION ST 6943 63000
GARDEN ST 7310 63000
SANTA BARBARA ST 1365 134400
LAGUNA ST 5381 63000
EMERSON AV 0 54600
Totals 210462 1805400 48401 47520 6 6 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-17
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily NS
Screenline 13 Cutline on Cliff Drive West of Meigs Rd
Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID RED ROSE WY 85 54600
ELISE WAY 1348 63000
CLIFF DR 10648 168000 10648 17190 8595 8595 3141_BA 3141_AB KENWOOD RD 1776 63000
Totals 13857 348600 10648 17190 1 1 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-18
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily NS
Screenline 101 Cutline on US-101 (Western Santa Barbara City Border) Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID AMTRAK 0 0
LA PALOMA AVE 0 54600
US HWY 101 SB 57861 239400 57861 61150 61150 1013_BA
VIEJA DR 932 54600
PALOMA DR 2 54600
MARINA DR 2010 67200
ESTRELLA DR 41 54600
MODOC RD 4260 67200
LAS PALMAS DR 2974 67200
LAGO DR 893 54600
US HWY 101 NB 66433 239400 66433 61150 61150 1013_AB
CALLE REAL 9258 134400 9258 10100 5050 5050 3192_BA 3192_AB PIRO LN 174 54600
STATE ST 16928 84000
9632 75600
CATHEDRAL OAKS RD 4293 84000 4293 13600 6800 6800 1300_BA 1300_AB Totals 175692 1386000 137845 146000 4 2 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-19
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily EW
Screenline 102 Cutline on SR-154 (Northern Santa Barbara City Border) Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID LA PALOMA AVE 0 54600
HWY 154 756 75600
HWY 154 18091 88200 18091 18300 9150 9150 1200_BA 1200_AB CIENEGUITAS RD 5842 75600
SAN ROQUE RD 6195 63000
CHELTENHAM RD 8178 63000
TUNNEL RD 5256 63000
STANWOOD DR 1087 67200
MISSION RIDGE RD 1672 67200
HILLCREST RD 0 54600
MOUNTAIN DR 1329 67200 1329 3500 1750 1750 1305_BA 1305_AB STANWOOD DR 639 67200
STANWOOD DR 1087 67200
COYOTE RD 297 63000
STANWOOD DR 1087 67200 1087 2700 1350 1350 1308_BA 1308_AB Totals 51517 1003800 20507 24500 3 3 0 0
ATTACHMENT 1
Model Development Report
For the City of Santa Barbara
Travel Demand Forecasting Development,
Documentation, and User’s Guide
Iteris, Inc. | Appendix G-20
Scenario Model Update and EIR SBCITY-2020Base_FINAL
Time Period Daily EW
Screenline 103 Cutline on PCH and SR-192 (Eastern Santa Barbara City Border) Roadway Roadway Volume Roadway Capacity
Roadway Volume
w/ Counts Roadway 2012 Count AB_COUNT BA_COUNT AB_COUNTID BA_COUNTID AMTRAK 0 0
US HWY 101 NB 43234 159600 43234 35050 35050 1000_AB
COAST VILLAGE RD 7228 63000
CHANNEL DR 71 63000
US HWY 101 SB 39824 159600 39824 35050 35050 1000_BA
1291 75600
MESA RD 9 54600
HOT SPRINGS RD 7307 63000
Totals 98964 638400 83058 70100 2 0 0 0
ATTACHMENT 1
City of Santa Barbara
2035 General Plan Update
Traffic Impact Analysis
December 7, 2023
Submitted to:

J# 11487 | Prepared by Iteris, Inc.

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Traffic Impact Analysis
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TABLE OF CONTENTS

1.0 Introduction..............................................................................................................................................4

1.1 Project Background.............................................................................................................................. 4

1.2 Study Area............................................................................................................................................5

1.3 Study Periods .......................................................................................................................................7

2.0 Environmental Setting.............................................................................................................................. 7

2.1 Roadway Configurations...................................................................................................................... 7

2.2 Existing Public Transit........................................................................................................................... 9

2.3 Existing Traffic Volumes....................................................................................................................... 9

3.0 Traffic Operations Analysis Methodology ................................................................................................9

4.0 Existing Conditions.................................................................................................................................10

5.0 Future Year 2035 With General Plan update .........................................................................................14

5.1 Future Year 2035 Traffic Volume Development ................................................................................14

5.2 Intersection LOS .................................................................................................................................17

6.0 Sensitivity Analysis .................................................................................................................................22

7.0 Conclusions.............................................................................................................................................24 Appendix A – Existing Traffic Volumes................................................................................................................ 25 Appendix B – LOS Calculation Sheets ................................................................................................................. 26 Appendix C – Model Development Report......................................................................................................... 28 Appendix D – Future Year 2035 Intersection Volumes.......................................................................................29 Appendix E – Sensitivity Analysis........................................................................................................................ 30 ATTACHMENT 2
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TABLES
Table 1: Intersection Level of Service Definitions...............................................................................................10 Table 2: Existing Peak Hour Intersection LOS ..................................................................................................... 11 Table 3: Proposed General Plan Update Net Land Use Changes........................................................................15 Table 4: Future Year 2035 With GPU Peak Hour Intersection LOS .....................................................................17 Table 5: Comparison of Impacted Intersections................................................................................................. 19

FIGURES
Figure 1 – Study Intersections .............................................................................................................................. 6 Figure 2 – Average Growth per region................................................................................................................ 16 Figure 3 – Impacted Study Intersections ............................................................................................................ 21 Figure 4 – Sensitivity Analysis Locations/Scenarios............................................................................................23

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1.0 INTRODUCTION
Thisreportsummarizesthe results of the Traffic Impact Analysis(TIA) for the City of Santa Barbara General Plan Update (GPU). This report provides detailed information concerning the methodology, findings and conclusions of the traffic analysis, as well as the development of an updated Citywide traffic model. The traffic analysis includes the evaluation of existing and future year traffic conditions at key intersections, taking into account growth in traffic due to buildout of the General Plan Update.

1.1 Project Background
During the 2011 Plan Santa Barbara process, the original Citywide traffic model, validated using traffic counts from 2008, was used to analyze various land use growth assumptions to help guide the decision‐ making process. The most conservative land use scenario was used in the Final Environmental Impact Report (EIR), because it was the most representative of the Council approved plan. This GPU effort results in the development of a new Citywide traffic model, adjusts the land use assumptions to match what was approved by Council in order to be more accurate. The Citywide traffic model was last updated in 2015, and the City aims to update it approximately every five to seven years in order to maintain accuracy. Year 2020 traffic volumes(pre‐pandemic) are evaluated asthe base year with this project,since traffic volumes have fluctuated during the pandemic. The traffic model forecasts will be based on the Plan Santa Barbara 2030 General Plan and the 2035 Climate Action Plan horizon years.

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1.2 Study Area
The study area for analysis of the proposed General Plan Update project’s effects on circulation includes 50 intersections throughout the City. The study intersections are shown in Figure 1 and are as follows:

1. SR‐154/Calle Real 26. De La Vina Street/Carrillo Street

2. SR‐154/US‐101 Southbound On‐Ramp 27. Bath Street/Carrillo Street

3. Calle Real/State Street 28. Castillo Street/Carrillo Street

4. La Cumbre Road/State Street 29. US‐101 Northbound Ramps/Carrillo Street

5. Las Palmas Drive‐La Cumbre Road/Calle Real 30. US‐101 Southbound Ramps/Carrillo Street

6. US‐101 Southbound Ramps/Las Palmas Drive 31. San Andres Street/Carrillo Street

7. Hope Avenue/State Street 32. Meigs Road/Cliff Drive
8 Hope Avenue/Calle Real 33. Castillo Street/US‐101 Northbound On‐Ramp‐Haley Street

9. Hitchcock Way/State Street 34. Castillo Street/US‐101 Southbound Ramps

10. Las Positas Road/State Street 35. Castillo Street/Montecito Street

11. US‐101 Northbound On‐Ramp/Calle Real 36. Castillo Street/Shoreline Drive‐Cabrillo Boulevard

12. Las Positas Road/Calle Real 37. Bath Street/Haley Street

13. Las Positas Road/US‐101 Southbound Ramps 38. Garden Street/Haley Street

14. Las Positas Road/Modoc Road 39. Garden Street/Gutierrez Street

15. De La Vina Street/State Street 40. Garden Street/US‐101 Northbound Ramps

16. Alamar Avenue/State Street 41. Garden Street/US‐101 Southbound Ramps

17. Constance Avenue/State Street 42. Garden Street/Yanonali Street

18. State Street/Mission Street 43. Milpas Street/Haley Street

19. De La Vina Street/Mission Street 44. Milpas Street/Gutierrez Street

20. Bath Street/Mission Street 45. Milpas Street/Quinientos Street

21. Castillo Street/Mission Street 46. Milpas Street/Carpinteria Street/US‐101 Northbound Ramps

22.US‐101 Northbound Ramps/Mission Street 47.Milpas Street/Alisos Street

23. US‐101 Southbound Ramps/Mission Street 48. Cabrillo Boulevard/US‐101 Ramps

24. Modoc Road/Mission Street 49.Hot Springs Road/Coast Village Road‐Old Coast Highway

25. Chapala Street/Carrillo Street 50. Olive Mill Road/Coast Village Road

ATTACHMENT 2
Figure 1
Study Intersection Locations
Legend
Signalized Intersection
X
X Stop-controlled Intersection
X Roundabout Intersection
N

NOT TO SCALE
City of Santa Barbara
2035 General Plan Update
Traffic Impact Analysis
State St
Calle Real
Las Positas
Mission St
Carrillo St
Haley St
Garden St
Cliff Dr
Castillo St
Milpas St
Olive Mill Rd
De La Vina St
154
La Cumbre Rd
192
State St
1 9
8 2 3 4
6 7
5 21 14
11 10
12 17
13 22
18 29
23 30
24 19
16 20
15 27
25 26
37 33
38 34
45 41
46 42
35 31
36 32
43 44
3940 47
48 49
50 28
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1.3 Study Periods
Traffic operations at the study intersections were evaluated for each of the following scenarios during the weekday a.m. (7:00 – 9:00) and p.m. (4:00 – 6:00) peak hours:
• Existing Conditions;
• Future Year 2035 With General Plan Update Conditions; In addition, several future year 2035 alternative scenarios were reviewed, which included modified circulation network assumptions. The details of these modifications are described later in the report.

2.0 ENVIRONMENTAL SETTING
This section presents an overview of the existing roadway and transit system within the study area, and the methodology used to determine existing traffic volumes.

2.1 Roadway Configurations
The existing configuration of key roadways within the study area are described below:
 Calle Real is a two‐lane roadway, running parallel to and north of US‐101, providing access to US‐ 101 via multiple interchanges. The roadway is discontinuous between State Street and La Cumbre Road. On‐street parking is prohibited on both sides of the roadway, with Class II bike lanes provided. The posted speed limit varies from 40 to 45 miles per hour.  State Street is oriented in an east‐west direction in the northwest portion of the City (Upper State neighborhood), where it is generally a four‐lane roadway. State Street transitions to a northwest‐ southeast orientation through the Oak Park, Downtown, and Lower State neighborhoods, where it is a two‐lane roadway. Class II bike lanes are provided from Calle Real on the north to Cabrillo Boulevard in the south. State Street provides access to US‐101 in the Upper State neighborhood.

On‐street parking is generally provided on both sides of the roadway throughout the City. The posted speed limit along State Street variesfrom 25 to 30 miles per hour. At the time of thisreport preparation, the City has implemented an interim State Street Promenade that is open to pedestrians and cyclists only, between Victoria Street and Haley Street, though remains open to emergency vehicles at all times and to delivery vehicles during limited times.  Las Positas Road is a three to four‐lane roadway oriented in a north‐south direction, providing access to US‐101 (northbound ramps via Calle Real). Between State Street and US‐101, on‐street parking is provided on the eastside of the street, with Class II bike lane provided on the westside.

The posted speed limit along Las Positas Road is 35 miles per hour. South of City limits, Las Positas Road transitions to a two‐lane roadway.
 Mission Street, oriented in a northeast‐southwest direction, is a four‐lane roadway southwest of De La Vina Street (providing accessto US‐101 via a diamond interchange) and a two‐lane roadway ATTACHMENT 2
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northeast of De La Vina Street. On‐street parking is provided on both sides of the street within the two‐lane portion of the roadway, while parking is prohibited within the four‐lane portion. The posted speed limit is generally 25 miles per hour.
 Carrillo Street, oriented in a northeast‐southwest direction, is a four‐lane roadway southwest of Santa Barbara Street and is a two‐lane roadway northeast of Santa Barbara Street. Access to US‐ 101 is provided via a diamond interchange. On‐street parking is generally provided within the Downtown area. ClassII bike lanes are provided west of San Andres Street. The posted speed limit along Carrillo Street is 25 miles per hour.
 Castillo Street is a one‐way, one‐lane roadway oriented in a southeast direction, northwest of Cota Street. The roadway includes on‐street parking, a Class II bike lane and provides access to US‐101. The posted speed limit in this segment is 25 miles per hour. Southeast of Cota Street, the roadway transitions to two‐way traffic flow.
 Garden Street is generally a two‐lane roadway oriented in a northwest‐southeast direction, providing access to US‐101 via a diamond interchange, though the roadway widens to a four‐lane roadway with a raised median southeast of Yanonali Street. On‐street parking is provided on both sides of the roadway within the two‐lane section. The posted speed limit along Garden Street is 25 miles per hour.
 Milpas Street is a two to four‐lane roadway oriented in a northwest‐southeast direction, providing access to US‐101. Milpas Street is a two‐lane roadway northwest of Cota Street, transitioning to a four‐lane roadway southeast of Cota Street. On‐street parking is generally provided on both sides of the roadway. The posted speed limit along Milpas Street varies from 25 to 30 miles per hour.
 La Cumbre Road is a two to four‐lane roadway oriented in a north‐south direction. North of State Street, La Cumbre Road is a two‐lane roadway with a posted speed limit of 30 to 35 miles per hour. South of State Street, La Cumbre Road is a four‐lane roadway, providing access to US‐101, with a posted speed limit of 30 miles per hour. South of US‐101, the roadway transitions to (and continues as) Las Palmas Drive.
 Coast Village Road is a two‐lane roadway, oriented in an east‐west direction, running north of and parallel to US‐101 between Hot Springs Road and Olive Mill Road. On‐street parking is provided, combination of parallel and angled, and the roadway has a posted speed limit of 25 miles per hour.
 Cabrillo Boulevard is a two to four‐lane roadway, generally oriented in an east‐west direction, providing access to US‐101. Between US‐101 and Ninos Drive, Cabrillo Boulevard is a two‐lane roadway with a posted speed limit of 35 miles per hour and Class II bike lanes in both directions.

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speed limit of 30 miles per hour. West of Castillo Street, the roadway transitionsto (and continues as) Shoreline Drive.

2.2 Existing Public Transit
The City is primarily served by bus lines provided by the Santa Barbara Metropolitan Transit District (MTD).

MTD operates 22 lines throughout the City, with average headways varying per line. Two lines that provide high frequency service (i.e., consisting of low average headways of 15 minutes each) are Line 1 (West Santa Barbara) and Line 2 (East Santa Barbara).. The other lines generally consist of headways ranging from 25 minutes to 75 minutes.

2.3 Existing Traffic Volumes
The year 2020 was selected by the project team and City as a baseline year for both the traffic analysis and existing model scenario. In order to develop the 2020 base year volumes (pre‐Covid), Iteris utilized 2015 and 2018 peak hour turning movement counts provided by the City in combination with historical traffic volume trends along state routes collected from Caltrans Performance Measurement System (PeMS) data. Based on the evaluation, an overall annual growth rate of 2.24% was calculated as a representative yearly growth in traffic within the City. This rate was applied to each intersection, based on the year that the original count was collected. The turning movement volumes are included in Appendix A.

3.0 TRAFFIC OPERATIONS ANALYSIS METHODOLOGY
The quality of traffic operationsis characterized using the concept of level ofservice (LOS). Level ofservice is defined by a range of grades from A (best) to F (worst). At intersections, LOS “A” represents relatively free operating conditions with little or no delay. LOS “F” is characterized by extremely unstable flow conditions and severe congestion with volumes at or near the intersection’s design capacity. This results in long queues backing up from all approaches to intersections.

Based on the City of Santa Barbara Traffic Analysis Guidelines, LOS calculations are based on the Intersection Capacity Utilization (ICU) method for signalized locations and the amount of delay (in seconds) for stop‐controlled intersections. Table 1 presents both the ICU range and the average delay associated with each LOS grade as well as a qualitative description of intersection operations at that grade.

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Table 1: Intersection Level of Service Definitions
Level
Of
Service
Description Signalized
ICU
Stop‐controlled
Average Vehicle
Delay (seconds)
A
Excellent operation. All approaches to the intersection appear quite open, turning movements are easily made, and nearly all drivers find freedom of operation.

0.00 – 0.60 < 10.0
B
Very good operation. Many drivers begin to feel somewhat restricted within platoons of vehicles. This represents stable flow. An approach to an intersection may occasionally be fully utilized and traffic queues start to form.

0.61 – 0.70 > 10.0 – 15.0
C
Good operation. Occasionally drivers may have to wait more than 60 seconds, and back‐ups may develop behind turning vehicles. Most drivers feel somewhat restricted.

0.71 – 0.80 > 15.0 – 25.0
D Fair operation. Cars are sometimes required to wait more than 60 seconds during short peaks. There are no long‐standing traffic queues. 0.81 – 0.90 > 25.0 – 35.0 E
Poor operation. Some long‐standing vehicular queues develop on critical approaches to intersections. Delays may be up to several minutes.

0.91 – 1.00 > 35.0 – 50.0
F
Forced flow. Represents jammed conditions. Backups form locations downstream or on the cross street may restrict or prevent movement of vehicles out of the intersection approach lanes; therefore, volumes carried are not predictable. Potential for stop and go type traffic flow. > 1.00 > 50.0
Source: Highway Capacity Manual
When evaluating traffic impacts of proposed development projects, the City identifies a signalized intersection as congested when the intersection operates at greater than 0.77 ICU (i.e., at or above the high end of the LOS C range) during peak travel times (morning and evening commute hours). This LOS range generally corresponds to an average vehicle delay of no greater than 25.0 seconds. Thus, this average delay upper limit of 25.0 seconds is used to assess traffic impacts at unsignalized or roundabout intersections.

4.0 EXISTING CONDITIONS
A level of service analysis was conducted to evaluate existing intersection operations during the a.m. and p.m. peak hours at study intersections. As mentioned, traffic counts used in this analysis were provided by the City. Table 2 summarizes the existing LOS at the study intersections. LOS calculation sheets are provided in Appendix B.

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Table 2: Existing Peak Hour Intersection LOS
Intersection
AM Peak Hour PM Peak Hour
ICU Delay (s) LOS ICU Delay (s) LOS
1 SR‐154/Calle Real 0.86 ‐ D 0.93 ‐ E
2 SR‐154/US‐101 SB On‐Ramp 0.60 ‐ A 0.56 ‐ A
3 Calle Real/State St 0.58 ‐ A 0.61 ‐ B
4 La Cumbre Rd/State St 0.49 ‐ A 0.62 ‐ B
5 Las Palmas Dr‐La Cumbre Rd/Calle Real 0.48 ‐ A 0.52 ‐ A 6 US‐101 SB Ramps/Las Palmas Dr 0.61 ‐ B 0.68 ‐ B
7 Hope Ave/State St 0.52 ‐ A 0.72 ‐ C
8 Hope Ave/Calle Real 0.60 ‐ A 0.73 ‐ C
9 Hitchcock Way/State St 0.52 ‐ A 0.69 ‐ B
10 Las Positas Rd/State St 0.61 ‐ B 0.72 ‐ C
11 US‐101 NB On‐Ramp/Calle Real 0.87 ‐ D 0.78 ‐ C
12 Las Positas Rd/Calle Real 0.83 ‐ D 0.77 ‐ C
13 Las Positas Rd/US‐101 SB Ramps 0.87 ‐ D 0.86 ‐ D
14 Las Positas Rd/Modoc Rd 0.93 ‐ E 0.82 ‐ D
15 De La Vina St/State St 0.53 ‐ A 0.71 ‐ C
16 Alamar Ave/State St 0.57 ‐ A 0.64 ‐ B
17 Constance Ave/State St 0.95 32.3 D 1.09 73.7 F
18 State St/Mission St 0.71 ‐ C 0.78 ‐ C
19 De La Vina St/Mission St 0.51 ‐ A 0.54 ‐ A
20 Bath St/Mission St 0.54 ‐ A 0.57 ‐ A
21 Castillo St/Mission St 0.53 ‐ A 0.59 ‐ A
22 US‐101 NB Ramps/Mission St 0.98 ‐ E 0.87 ‐ D
23 US‐101 SB Ramps/Mission St 1.06 ‐ F 1.03 ‐ F
24 Modoc Rd/Mission St 0.87 24.2 C 0.99 35.3 E
25 Chapala St/Carrillo St 0.49 ‐ A 0.73 ‐ C
26 De La Vina St/Carrillo St 0.79 ‐ C 0.80 ‐ C
27 Bath St /Carrillo St 0.68 ‐ B 0.72 ‐ C
28 Castillo St/Carrillo St 0.83 ‐ D 0.83 ‐ D
29 US‐101 NB Ramps/Carrillo St 0.85 ‐ D 1.04 ‐ F
30 US‐101 SB Ramps/Carrillo St 0.93 ‐ E 0.84 ‐ D
31 San Andres St/Carrillo St 0.79 ‐ C 0.81 ‐ D
32 Meigs Rd/Cliff Dr 0.69 ‐ B 0.72 ‐ C
33 Castillo St/US‐101 NB On‐Ramp‐Haley St 0.62 ‐ B 0.82 ‐ D 34 Castillo St/US‐101 SB Ramps 0.88 ‐ D 0.94 ‐ E
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Intersection
AM Peak Hour PM Peak Hour
ICU Delay (s) LOS ICU Delay (s) LOS
35 Castillo St/Montecito St 0.71 ‐ C 0.79 ‐ C
36 Castillo St/Shoreline Dr‐Cabrillo Blvd 0.31 ‐ A 0.47 ‐ A 37 Bath St/Haley St 0.63 ‐ B 0.69 ‐ B
38 Garden St/Haley St 0.77 ‐ C 0.75 ‐ C
39 Garden St/Gutierrez St 0.81 ‐ D 0.78 ‐ C
40 Garden St/US‐101 NB Ramps 0.72 ‐ C 0.93 ‐ E
41 Garden St/US‐101 SB Ramps 0.64 ‐ B 0.68 ‐ B
42 Garden St/Yanonali St 0.57 ‐ A 0.58 ‐ A
43 Milpas St/Haley St 0.46 ‐ A 0.63 ‐ B
44 Milpas St/Gutierrez St 0.53 ‐ A 0.61 ‐ B
45 Milpas St/Quinientos St 0.54 ‐ A 0.57 ‐ A
46 Milpas St/Carpinteria St/US‐101 NB Ramps* ‐ 17.2 C ‐ 14.9 B 47 Milpas St/Alisos St (US‐101 SB Ramps)/Hutash St 0.49 ‐ A 0.51 ‐ A 48 Cabrillo Blvd/US‐101 Ramps 0.70 ‐ B 0.83 ‐ D
49 Hot Springs Rd/Old Coast Hwy‐Coast Village Rd* ‐ 10.1 B ‐ 23.3 C 50 Olive Mill Rd/Coast Village Rd 0.93 40.4 E 1.18 67.7 F * Roundabout intersection
Notes:

V/C = Volume‐to‐Capacity ratio using ICU method; s = seconds, LOS = Level of Service.

As shown in Table 2, the following study intersections are currently operating at a congested level (V/C ratio above 0.77 for signalized intersections and delay above 25 seconds for unsignalized intersections):
 SR‐154/Calle Real (a.m. and p.m. peak hour);
 US‐101 Northbound On‐Ramp/Calle Real (a.m. and p.m. peak hour);  Las Positas Road/Calle Real (a.m. peak hour);
 Las Positas Road/US‐101 Southbound Ramps (a.m. and p.m. peak hour);  Las Positas Road/Modoc Road (a.m. and p.m. peak hour);  Constance Avenue/State Street (a.m. and p.m. peak hour);  State Street/Mission Street (p.m. peak hour);
 US‐101 Northbound Ramps/Mission Street (a.m. and p.m. peak hour);  US‐101 Southbound Ramps/Mission Street (a.m. and p.m. peak hour);  Modoc Road/Mission Street (p.m. peak hour);
 De La Vina Street/Carrillo Street (a.m. and p.m. peak hour);  Castillo Street/Carrillo Street (a.m. and p.m. peak hour);  US‐101 Northbound Ramps/Carrillo Street (a.m. and p.m. peak hour);  US‐101 Southbound Ramps/Carrillo Street (a.m. and p.m. peak hour);  San Andres Street/Carrillo Street (a.m. and p.m. peak hour); ATTACHMENT 2
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 Castillo Street/US‐101 Northbound On‐Ramp‐Haley Street (p.m. peak hour);  Castillo Street/US‐101 Southbound Ramps (a.m. and p.m. peak hour);  Castillo Street/Montecito Street (p.m. peak hour):
 Garden Street/Gutierrez Street (a.m. and p.m. peak hour);  Garden Street/US‐101 Northbound Ramps (p.m. peak hour);  Cabrillo Boulevard/US‐101 Ramps (p.m. peak hour); and  Olive Mill Road/Coast Village Road (a.m. and p.m. peak hour).

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5.0 FUTURE YEAR 2035 WITH GENERAL PLAN UPDATE
This section presents the analysis of traffic operations with the proposed General Plan Update in future year 2035 conditions, as well as the methodology used to develop future year 2035 traffic volumes.

5.1 Future Year 2035 Traffic Volume Development
Future year 2035 traffic volumes were developed using the City of Santa Barbara Travel Model (SBTM), a focused city‐wide model consistent with regional and subregional models, including the base‐year and forecast year land‐use projections and transportation networks. The SBTM was developed as a refined model for the City of Santa Barbara, built from the current SBCAG regional model including the full Santa Barbara County. The SBTM was developed to address the City of Santa Barbara General Plan Update, transportation impact assessments, vehicle miles traveled (VMT) forecasts for CEQA analysis, and air quality greenhouse gas (GHG) applications.

Detailed information on how the model was developed and validated is provided in the Model Development Report provided in Appendix C. As noted in the report, the previous version of the City of Santa Barbara model was originally validated to the year 2008. This new model version reflects an update of the base year to 2020. The updated travel demand model was developed using the TransCAD software package. The forecasting model for the City of Santa Barbara is consistent with the traditional four‐step modeling process, which includes trip generation, trip distribution, mode split, and traffic assignment.

The transportation analysis zone (TAZ) structure for the SBTM was obtained through coordination with the City of Santa Barbara, and use of the SBCAG and previous City of Santa Barbara model TAZ structures.

After zone refinement, the SBTM has a total of 1202 zones. The GPU buildout land use plan, developed by the City was included within the appropriate TAZ’s in the model. Table 3 summarizes the General Plan Update’s proposed net changes in land use.

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Table 3: Proposed General Plan Update Net Land Use Changes Land Use Type Existing
(2020)
Proposed Land Use Plan
(2035) Net Change
Residential
Residential Units 37,621 units 43,236 units +5,615 units Population 88,485 residents 100,713 residents +12,228 residents Non‐Residential
Employment 77,153 jobs 82,867 jobs +5,714 jobs
As shown, the GPU’s anticipated change in land use over the 2035 estimated buildout is:
 Addition of 5,615 residential units and 12,228 residents; and  Addition of 5,714 jobs.

In addition to buildout land use, the GPU includes circulation network modifications. These modifications are as follows:
 US‐101 High Occupancy Vehicle project, which includes extension of the existing HOV facility from Casitas Pass Road (outside the City) to the Hot Springs Road/Cabrillo Road interchange.  Anacapa Street is modified to a two‐way roadway between Haley Street and Gutierrez Street.

5.1.1 Post‐processing
Year 2035 traffic forecasts, incorporating the General Plan land use, were developed using a post‐ processing methodology. The base year (2020) model scenario and future year 2035 with GPU model scenario volumes were compared to determine the growth/volume change at each intersection. This change was then screened for reasonableness, as some locations showed a reduction in volume at individual movements. Where growth appeared to be reasonable, the change in model volumes were added to the existing turning movement counts. Where growth did not appear to be reasonable, an average growth rate was applied instead. For the purposes of this exercise, the 50 study intersections were aggregated into three regions, in order to provide a more representative growth forecast (as opposed to one growth rate for the whole City). The resulting growth in traffic per region is shown in Figure 2.

ATTACHMENT 2
Figure 2
Average Model Volume Growth per Region
Legend
Signalized Intersection
X
X Stop-controlled Intersection
X Roundabout Intersection
N

NOT TO SCALE
City of Santa Barbara
2035 General Plan Update
Traffic Impact Analysis
State St
Calle Real
Las Positas
Mission St
Carrillo St
Haley St
Garden St
Cliff Dr
Castillo St
Milpas St
Olive Mill Rd
De La Vina St
154
La Cumbre Rd
192
State St
1 2
3 AM Growth: 7%
PM Growth: 6%
AM Growth: 8%
PM Growth: 8%
AM Growth: 15%
PM Growth: 18%
1 9
8 2 3 4
6 7
5 21 14
11 10
12 17
13 22
18 29
23 30
24 19
16 20
15 27
25 26
37 33
38 34
45 41
46 42
35 31
36 32
43 44
3940 47
48 49
50 28
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5.2 Intersection LOS
Based on the volume development process described, the future year 2035 with General Plan Update peak hour intersection volumes are provided in Appendix D. As part of the traffic analysis, the following intersection‐specific modifications are included in the future year 2035 scenario:
 Milpas Street/US‐101 Southbound Ramps/Hutash Street (#47) – A dedicated southbound left‐ turn phase from Milpas Street to Hutash Street would be added, with two separate southbound left‐turn lanes serving the US‐101 Southbound On‐ramp. The southbound left‐turn phase onto the on‐ramp would be overlapped with the US‐101 Southbound Off‐ramp phase (northwest direction).
 Olive Mill Road/Coast Village Road (#50) – The intersection would be modified from a stop‐ controlled intersection to a roundabout. Note that at the time of this report, construction of the roundabout has been mostly completed.

The lane configurations and traffic control at all other intersections are assumed to be similar to existing conditions. Table 4 summarizes the future year 2035 with project LOS at the study intersections. LOS calculation sheets are included in Appendix B.

Table 4: Future Year 2035 With GPU Peak Hour Intersection LOS Intersection
AM Peak Hour PM Peak Hour
V/C Delay (s) LOS V/C Delay (s) LOS
1 SR‐154/Calle Real 0.95 ‐ E 0.95 ‐ E
2 SR‐154/US‐101 SB On‐Ramp 0.62 ‐ B 0.41 ‐ A
3 Calle Real/State St 0.62 ‐ B 0.64 ‐ B
4 La Cumbre Rd/State St 0.52 ‐ A 0.64 ‐ B
5 Las Palmas Dr‐La Cumbre Rd/Calle Real 0.50 ‐ A 0.55 ‐ A 6 US‐101 SB Ramps/Las Palmas Dr 0.66 ‐ B 0.72 ‐ C
7 Hope Ave/State St 0.56 ‐ A 0.76 ‐ C
8 Hope Ave/Calle Real 0.67 ‐ B 0.77 ‐ C
9 Hitchcock Way/State St 0.56 ‐ A 0.74 ‐ C
10 Las Positas Rd/State St 0.66 ‐ B 0.79 ‐ C
11 US‐101 NB On‐Ramp/Calle Real 0.90 ‐ D 0.82 ‐ D
12 Las Positas Rd/Calle Real 0.86 ‐ D 0.81 ‐ D
13 Las Positas Rd/US‐101 SB Ramps 0.92 ‐ E 0.91 ‐ E
14 Las Positas Rd/Modoc Rd 0.99 ‐ E 0.87 ‐ D
15 De La Vina St/State St 0.56 ‐ A 0.77 ‐ C
16 Alamar Ave/State St 0.62 ‐ B 0.69 ‐ B
17 Constance Ave/State St 1.09 46.0 E 1.20 106.1 F
ATTACHMENT 2
Santa Barbara 2035 GPU
Traffic Impact Analysis
Iteris, Inc. | 18
Intersection
AM Peak Hour PM Peak Hour
V/C Delay (s) LOS V/C Delay (s) LOS
18 State St/Mission St 0.77 ‐ C 0.86 ‐ D
19 De La Vina St/Mission St 0.55 ‐ A 0.59 ‐ A
20 Bath St/Mission St 0.57 ‐ A 0.62 ‐ B
21 Castillo St/Mission St 0.56 ‐ A 0.65 ‐ B
22 US‐101 NB Ramps/Mission St 1.04 ‐ F 0.94 ‐ E
23 US‐101 SB Ramps/Mission St 1.13 ‐ F 1.12 ‐ F
24 Modoc Rd/Mission St 0.97 31.2 D 1.06 43.8 E
25 Chapala St/Carrillo St 0.54 ‐ A 0.80 ‐ C
26 De La Vina St/Carrillo St 0.84 ‐ D 0.84 ‐ D
27 Bath St /Carrillo St 0.71 ‐ C 0.75 ‐ C
28 Castillo St/Carrillo St 0.86 ‐ D 0.85 ‐ D
29 US‐101 NB Ramps/Carrillo St 0.91 ‐ E 1.11 ‐ F
30 US‐101 SB Ramps/Carrillo St 0.97 ‐ E 0.91 ‐ E
31 San Andres St/Carrillo St 0.85 ‐ D 0.86 ‐ D
32 Meigs Rd/Cliff Dr 0.74 ‐ C 0.78 ‐ C
33 Castillo St/US‐101 NB On‐Ramp‐Haley St 0.68 ‐ B 0.88 ‐ D 34 Castillo St/US‐101 SB Ramps 0.94 ‐ E 0.98 ‐ E
35 Castillo St/Montecito St 0.73 ‐ C 0.86 ‐ D
36 Castillo St/Shoreline Dr‐Cabrillo Blvd 0.34 ‐ A 0.49 ‐ A 37 Bath St/Haley St 0.70 ‐ B 0.75 ‐ C
38 Garden St/Haley St 0.85 ‐ D 0.82 ‐ D
39 Garden St/Gutierrez St 0.82 ‐ D 0.85 ‐ D
40 Garden St/US‐101 NB Ramps 0.80 ‐ C 1.05 ‐ F
41 Garden St/US‐101 SB Ramps 0.75 ‐ C 0.74 ‐ C
42 Garden St/Yanonali St 0.64 ‐ B 0.69 ‐ B
43 Milpas St/Haley St 0.48 ‐ A 0.68 ‐ B
44 Milpas St/Gutierrez St 0.57 ‐ A 0.66 ‐ B
45 Milpas St/Quinientos St 0.59 ‐ A 0.63 ‐ B
46 Milpas St/Carpinteria St/US‐101 NB Ramps* ‐ 39.1 E ‐ 40.9 E 47 Milpas St/Alisos St (US‐101 SB Ramps)/Hutash St 0.50 ‐ A 0.65 ‐ B 48 Cabrillo Blvd/US‐101 Ramps 0.77 ‐ C 0.94 ‐ E
49 Hot Springs Rd/Old Coast Hwy‐Coast Village Rd* ‐ 12.9 B ‐ 61.7 F 50 Olive Mill Rd/Coast Village Rd* ‐ 10.8 B ‐ 18.8 C * Roundabout intersection
Notes:

V/C = Volume‐to‐Capacity ratio using ICU method; s = seconds, LOS = Level of Service.

ATTACHMENT 2
Santa Barbara 2035 GPU
Traffic Impact Analysis
Iteris, Inc. | 19
As shown in Table 4, a total of 27 intersections are forecast to be impacted in future year 2035 with buildout of the General Plan Update, which include both signalized and unsignalized/roundabout intersections. The 27 intersections are highlighted in Figure 3. A key component of this project is to determine how this new analysis aligns with the City of Santa Barbara’s Traffic Management Strategy.

Thus, a comparison of these 27 impacted intersections from the 2035 GPU to the 27 impacted intersections identified within the City’s currently adopted Traffic Analysis Guidelines (May 2020) is provided in Table 5.

Table 5: Comparison of Impacted Intersections
Impacted Intersection in
2035 GP Update
Also impacted in Traffic
Analysis Guidelines
(May 2020)?
#1 SR‐154/Calle Real No
#10 Las Positas Rd/State St Yes
#11 US‐101 NB On‐ramp/Calle Real Yes
#12 Las Positas Rd/Calle Real No
#13 Las Positas Rd/US‐101 SB Ramps Yes
#14 Las Positas Rd/Modoc Rd Yes
#17 Constance Ave/State St No
#18 State St/Mission St Yes
#22 US‐101 NB Ramps/Mission St Yes
#23 US‐101 SB Ramps/Mission St Yes
#24 Modoc Rd/Mission St Yes
#25 Chapala St/Carillo St No
#26 De La Vina St/Carillo St No
#28 Castillo St/Carillo St No
#29 US‐101 NB Ramps/Carillo St Yes
#30 US‐101 SB Ramps/Carillo St Yes
#31 San Andres St/Carillo St Yes
#32 Meigs Rd/Cliff Dr Yes
#33 Castillo St/US‐101 NB On‐ramp/Haley St Yes
#34 Castillo St/US‐101 SB Ramps No
#35 Castillo St/Montecito St No
#38 Garden St/Haley St No
#39 Garden St/Gutierrez St Yes
#40 Garden St/US‐101 SB Ramps Yes
#46 Milpas St/Carpinteria St/US‐101 NB Ramps* No
#48 Cabrillo Blvd/US‐101 SB Ramps No
ATTACHMENT 2
Santa Barbara 2035 GPU
Traffic Impact Analysis
Iteris, Inc. | 20
Impacted Intersection in
2035 GP Update
Also impacted in Traffic
Analysis Guidelines
(May 2020)?
#49 Hot Springs Rd/Old Coast Hwy‐Coast Village Rd* Yes
* Roundabout intersection
As shown in Table 5, 16 of the 27 impacted intersections were identified within the City’s previous list of impacted intersections. Thus, 11 new locations are identified as part of this analysis. Note that the purpose of the City’s Traffic Management Strategy is to ensure that no one land development project uses a disproportionate share of the limited remaining traffic capacity at impacted intersections.

ATTACHMENT 2
Figure 3
Impacted Study Intersections
Legend
Impacted Intersection
*
X
X Non-impacted Intersection
*
*
* Location was also impacted in May 2020 Traffic Impact Guidelines *
*
*
*
*
*
*
*
*
*
*
*
*
*
N

6.0 SENSITIVITY ANALYSIS
As mentioned, in addition to the future year 2035 with General Plan Update scenario, other potential circulation network scenarios were reviewed for sensitivity testing purposes. Five potential circulation network scenarios were modeled in order to determine their effect on traffic patterns within the vicinity.

The scenarios are described as follows:

1. Without US‐101 HOV project;

2. Closure of 400 State Street block, without US‐101 HOV project;

3. Conversion of Gutierrez Street from one‐way to two‐way operations (between Santa Barbara Street and Chapala Street) and closure of 400 State Street block, without US‐101 HOV project;

4. Closure of 400 State Street block, with US‐101 HOV project; and

5. Conversion of Gutierrez Street from one‐way to two‐way operations and closure of 400 State Street block, with US‐101 HOV project.

Thus, the future year 2035 with General Plan Update scenario (described in Section 5) is considered Scenario 0 in this sensitivity analysis. Study intersections within the vicinity of these modifications were evaluated to determine peak hour volume changes that are anticipated, as compared to the future year 2035 with GPU scenario (scenario 0) described earlier. Based on those changes, the overall level of impact was determined, considering the City’s threshold for traffic operations. Figure 3 shows the locations of the circulation network modifications, as well as the intersections evaluated for sensitivity. As shown, model volume outputs at 10 intersections were reviewed, with these intersectionslocated primarily along Haley Street, Gutierrez Street, Garden Street, and/or Chapala Street.

ATTACHMENT 2
Mission St
Carrillo St
Haley St
Garden St
Castillo St
Milpas St
De La Vina St
State St
25 37
33 38
34 41
43 44
3940
Gutierrez St
Santa Barbara St
Anacapa S
Chapala St
t
Figure 4
Sensitivity Analysis Locations/Scenarios
Close to impact in AM & PM
(Scenario 0)
US-101 HOV Project
Close to impact in PM
(Scenario 0)
N

NOT TO SCALE
City of Santa Barbara
2035 General Plan Update
Traffic Impact Analysis
* Anacapa St is assumed to be a 2-way roadway from
Haley St to Gutierrez St in all future scenarios

0. With HOV (2035 With GP “Baseline”)

1. Without HOV

2. 400 State St closure alone (without HOV)

3. Gutierrez as a 2-way & 400 State St closure together (without HOV)

4. 400 State St closure alone (with HOV)

5. Gutierrez as a 2-way & 400 State St closure together (with HOV) 2035 With GP Scenarios*
ATTACHMENT 2
Santa Barbara 2035 GPU
Traffic Impact Analysis
Iteris, Inc. | 24
The following summarizes the results of the sensitivity analysis:
 In all cases, the change in model turning movement volumes between scenarios 1 through 5 and scenario 0 was minimal (increases and decreases), thus would not result in triggering of a new intersection impact.
 At Bath Street/Haley Street (#37), the intersection is forecast to operate at an ICU of 0.75 in the p.m. peak hour, which is near the 0.77 threshold. The increases in individual turning movement volumes (in scenarios 1 through 5) were not large enough to increase critical movement volume‐ to‐capacity ratios such that the overall intersection ICU would trigger an impact (i.e., an ICU of greater than 0.77).
 Similarly, at Garden Street/US‐101 Southbound Ramps (#41), the intersection is forecast to operate at an ICU of 0.75 in the a.m. peak hour and 0.74 in the p.m. peak hour. Increases in individual turning movement volumes, in both time periods, were also not large enough to result in an overall intersection ICU of greater than 0.77.
 At other intersections that were already forecast to be impacted in scenario 0, any forecast reductions in individual turning movement volumes (in scenarios 1 through 5) were not large enough to result in an elimination of the intersection’s impact (i.e., an ICU of 0.77 or less).

The resulting comparison of model turning movement volumes, at the intersections, from the various scenarios is shown in Appendix E.

7.0 CONCLUSIONS
This report consists of detailed information concerning the methodology, findings and conclusions of the traffic analysis, as well as the development of an updated Citywide traffic model. The following describes the key conclusions from the study:
 In existing conditions, 24 study intersections are currently operating at a congested level (V/C ratio above 0.77 for signalized intersections and delay above 25 seconds for unsignalized or roundabout intersections);
 Iteris developed the City of Santa Barbara Travel Model (SBTM), a focused citywide model consistent with regional and subregional models, for use in evaluating the General Plan Update as well as other land development or transportation projects moving forward;  The proposed General Plan Update buildout land use plan, developed by the City, was included within the appropriate TAZ’s in the model;
 A total of 27 intersections are forecast to be impacted in future year 2035 with buildout of the General Plan Update. Of the 27 impacted intersections, 16 were identified within the City’s previous list of impacted intersections. Thus, 11 new locations are identified as part of this analysis.

ATTACHMENT 2
City of Santa Barbara
2035 General Plan Update
Traffic Impact Analysis
Technical Appendix
Submitted to:

J# 11487 | Prepared by Iteris, Inc.

ATTACHMENT 2
Santa Barbara 2035 GPU
Traffic Impact Analysis
Iteris, Inc.

APPENDIX A – EXISTING TRAFFIC VOLUMES
ATTACHMENT 2
City of Santa Barbara
General Plan Update
Appendix A
Existing Volumes
Existing AM Peak Hour Volumes
Left Thru Right Left Thru Right Left Thru Right Left Thru Right 1 SR‐154/Calle Real 69 218 200 121 764 296 53 140 303 156 191 503 2 SR‐154/US‐101 SB On‐Ramp 0 487 212 961 262 0000000
3 Calle Real/State Street 205 622 108 188 0 31 36 534 0 0 775 230 4 La Cumbre Road/State Street 135 169 89 128 224 105 97 380 84 87 445 58 5 Las Palmas Drive/La Cumbre Road/Calle Real 296 0 342 0 0 0 0 494 482 80 604 0 6 US‐101 SB Ramps/Las Palmas Drive 0 0 0 536 14 218 0 440 207 470 430 0 7 Hope Avenue/State Street 50 171 50 102 174 166 88 631 41 36 610 68 8 Hope Avenue/Calle Real 434 240 123 58 85 75 155 287 141 111 161 63 9 Hitchcock Way/State Street 77 12 166 7 4 3 17 726 86 176 615 11 10 Las Positas Road/State Street 183 211 335 119 241 87 64 697 169 255 572 45 11 US‐101 NB On‐Ramp/Calle Real 0 0 0 7 11 7 6 231 6 1066 263 6 12 Las Positas Road/Calle Real 766 759 0 1 591 320 67 0 174 336 261 351 13 Las Positas Road/US‐101 SB Ramps 1 1165 257 483 618 0 360 1 302 0 0 0 14 Las Positas Road/Modoc Road 120 810 37 162 536 222 253 182 147 31 139 360 15 De La Vina Street/State Street 196 0 18 2 0 0 4 618 496 3 613 4 16 Alamar Avenue/State Street 87 182 54 188 224 20 47 502 36 40 430 79 17 Constance Avenue/State Street 8 56 8 11 47 327 429 315 11 9 210 8 18 State Street/Mission Street 99 198 22 23 320 61 44 360 131 17 362 30 19 De La Vina Street/Mission Street 0 0 0 43 473 291 0 654 353 22 545 0 20 Bath Street/Mission Street 175 134 44 43 0 88 88 818 0 0 737 43 21 Castillo Street/Mission Street 0 0 0 21 39 167 125 828 187 7 974 24 22 US‐101 NB Ramps/Mission Street 143 2 224 0 0 0 582 1098 0 0 605 605 23 US‐101 SB Ramps/Mission Street 0 0 0 1120 2 153 0 560 197 396 352 0 24 Modoc Road/Mission Street 0 0 0 244 0 178 215 391 0 2 234 162 25 Chapala Street/Carrillo Street 165 213 33 0 0 0 426 833 119 0 338 62 26 De La Vina Street/Carrillo Street 0 0 0 114 375 187 0 1281 173 20 482 0 27 Bath Street /Carrillo Street 140 176 46 0 0 0 204 1494 0 0 602 58 28 Castillo Street/Carrillo Street 0 0 0 56 138 244 0 1679 153 14 717 0 29 US‐101 NB Ramps/Carrillo Street 343 0 240 0 0 0 384 1583 0 0 472 485 30 US‐101 SB Ramps/Carrillo Street 0 0 0 1103 2 344 0 864 502 174 641 0 31 San Andres Street/Carrillo Street 63 130 146 248 161 28 20 823 53 84 554 99 32 Meigs Road/Cliff Drive 240 169 58 248 142 168 231 427 200 33 318 128 33 Castillo Street/US‐101 NB On‐Ramp/Haley Street 463 70 417 53 176 90 0 0 0 338 125 22 34 Castillo Street/US‐101 SB Ramps 0 752 272 80 434 0 198 1 657 0 0 0 35 Castillo Street/Montecito Street 37 248 10 72 322 695 667 199 61 17 182 102 36 Castillo Street/Shoreline Drive/Cabrillo Boulevard 0 0 0 65 0 156 99 250 0 0 267 76 37 Bath Street/Haley Street 221 176 52 0 0 0 111 371 21 7 244 64 38 Garden Street/Haley Street 0 424 395 26 361 0 12 432 199 0 0 0 39 Garden Street/Gutierrez Street 375 781 0 0 558 34 0 0 0 485 385 33 40 Garden Street/US‐101 NB Ramps 179 848 0 0 530 513 0 0 0 163 0 308 41 Garden Street/US‐101 SB Ramps 0 319 89 314 379 0 708 0 467 0 0 0 42 Garden Street/Yanonali Street 18 92 31 417 125 304 156 165 15 12 110 160 43 Milpas Street/Haley Street 0 684 31 9 746 0 88 98 195 0 0 0 44 Milpas Street/Gutierrez Street 107 655 24 45 735 101 0 0 0 83 180 56 45 Milpas Street/Quinientos Street 61 763 18 62 811 28 17 25 63 123 31 81 46 Milpas Street/Carpinteria Street/US‐101 NB Ramps 186 381 79 54 552 459 0 0 0 78 264 103 47 Milpas Street/Hutash Street‐US‐101 SB Ramps 0 221 45 567 292 0 0 0 0 33 0 296 48 Cabrillo Boulevard/US‐101 Ramps 0 218 0 0 124 0 603 0 2 123 0 1 49 Hot Springs Road/Old Coast Highway/Coast Village Road 19 434 320 18 419 40 131 202 18 282 76 41 50 Olive Mill Road/Coast Village Road 112 217 18 94 57 178 144 179 62 48 102 129 Westbound
1 Region
2 3
# Intersection
Northbound Southbound Eastbound
ATTACHMENT 2
City of Santa Barbara
General Plan Update
Appendix A
Existing Volumes
Existing PM Peak Hour Volumes
Left Thru Right Left Thru Right Left Thru Right Left Thru Right 1 SR‐154/Calle Real 73 278 152 91 551 145 61 122 305 164 304 741 2 SR‐154/US‐101 SB On‐Ramp 0 503 234 815 205 0000000
3 Calle Real/State Street 222 676 117 195 0 31 31 612 0 0 824 338 4 La Cumbre Road/State Street 213 202 166 165 286 76 139 527 126 183 646 119 5 Las Palmas Drive/La Cumbre Road/Calle Real 326 0 437 0 0 0 0 524 506 116 727 0 6 US‐101 SB Ramps/Las Palmas Drive 0 0 0 633 8 153 0 397 138 600 453 0 7 Hope Avenue/State Street 58 302 111 131 154 187 143 790 52 98 825 146 8 Hope Avenue/Calle Real 487 271 140 125 124 138 180 304 165 174 178 48 9 Hitchcock Way/State Street 128 13 255 37 22 12 29 911 98 238 924 21 10 Las Positas Road/State Street 207 211 428 83 177 61 90 922 240 337 885 94 11 US‐101 NB On‐Ramp/Calle Real 0 0 0 8 0 8 7 388 10 866 322 14 12 Las Positas Road/Calle Real 420 801 0 0 627 284 72 0 319 461 476 403 13 Las Positas Road/US‐101 SB Ramps 0 734 215 582 825 0 487 6 512 0 0 0 14 Las Positas Road/Modoc Road 152 565 44 302 734 301 227 189 145 36 105 157 15 De La Vina Street/State Street 379 2 25 11 0 12 9 812 620 1 916 19 16 Alamar Avenue/State Street 144 238 98 95 185 42 42 627 67 80 635 92 17 Constance Avenue/State Street 3 75 14 9 85 415 427 372 17 26 384 3 18 State Street/Mission Street 143 300 23 21 347 63 56 364 134 20 342 61 19 De La Vina Street/Mission Street 0 0 0 53 620 287 0 577 265 63 687 0 20 Bath Street/Mission Street 148 135 31 80 0 161 60 623 0 0 842 44 21 Castillo Street/Mission Street 0 0 0 18 95 196 68 692 295 25 1133 24 22 US‐101 NB Ramps/Mission Street 200 1 208 0 0 0 384 974 0 0 818 634 23 US‐101 SB Ramps/Mission Street 0 0 0 890 6 339 0 468 164 451 567 0 24 Modoc Road/Mission Street 0 0 0 234 0 171 174 362 0 1 480 249 25 Chapala Street/Carrillo Street 471 596 89 0 0 0 414 600 221 0 571 135 26 De La Vina Street/Carrillo Street 0 0 0 140 462 419 1 1108 145 39 1000 0 27 Bath Street /Carrillo Street 193 268 72 0 0 0 251 1171 0 0 1273 108 28 Castillo Street/Carrillo Street 0 0 0 68 193 321 2 1413 220 24 1456 0 29 US‐101 NB Ramps/Carrillo Street 428 1 188 0 0 0 295 1429 0 0 935 848 30 US‐101 SB Ramps/Carrillo Street 0 0 0 891 4 547 0 833 286 230 1133 0 31 San Andres Street/Carrillo Street 53 160 79 228 173 42 20 544 46 90 944 205 32 Meigs Road/Cliff Drive 274 183 44 228 253 210 211 408 230 62 386 135 33 Castillo Street/US‐101 NB On‐Ramp/Haley Street 661 85 553 56 220 117 0 0 0 512 240 14 34 Castillo Street/US‐101 SB Ramps 0 1068 233 87 645 0 231 3 613 0 0 0 35 Castillo Street/Montecito Street 70 386 17 133 398 730 744 216 80 21 210 153 36 Castillo Street/Shoreline Drive/Cabrillo Boulevard 0 0 0 124 0 191 197 350 0 0 417 111 37 Bath Street/Haley Street 253 152 62 0 0 0 134 457 28 3 512 65 38 Garden Street/Haley Street 0 321 305 36 577 0 24 649 377 0 0 0 39 Garden Street/Gutierrez Street 291 535 0 0 939 73 0 0 0 551 428 45 40 Garden Street/US‐101 NB Ramps 348 642 0 0 708 782 0 0 0 136 0 184 41 Garden Street/US‐101 SB Ramps 0 511 159 473 371 0 479 2 320 0 0 0 42 Garden Street/Yanonali Street 23 152 15 232 149 310 291 163 25 4 156 227 43 Milpas Street/Haley Street 0 609 58 8 874 0 141 219 401 0 0 0 44 Milpas Street/Gutierrez Street 85 609 52 111 1065 116 0 0 0 79 142 74 45 Milpas Street/Quinientos Street 22 629 43 204 919 31 9 39 65 88 20 86 46 Milpas Street/Carpinteria Street/US‐101 NB Ramps 249 374 133 76 566 414 0 0 0 97 199 110 47 Milpas Street/Hutash Street‐US‐101 SB Ramps 0 420 66 441 379 0 0 0 0 53 0 271 48 Cabrillo Boulevard/US‐101 Ramps 0 518 0 0 153 0 558 0 2 90 0 8 49 Hot Springs Road/Old Coast Highway/Coast Village Road 21 416 584 34 430 102 195 359 15 409 106 57 50 Olive Mill Road/Coast Village Road 135 96 44 138 73 112 405 404 109 30 79 66 Intersection
Northbound Southbound Eastbound Westbound
1 Region
2 3
#
ATTACHMENT 2
Santa Barbara 2035 GPU
Traffic Impact Analysis
Iteris, Inc.

APPENDIX B – LOS CALCULATION SHEETS
ATTACHMENT 2
Santa Barbara 2035 GPU
Traffic Impact Analysis
Iteris, Inc.

Existing Conditions
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 1
Intersection: SR-154/Calle Real
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : Y
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 296 0 0.00 N‐S(1): 0.21
TH 2.00 764 3,200 0.33 * N‐S(2): 0.37 *
LT 1.00 121 1,600 0.08 E‐W(1): 0.39 *
Westbound RT 1.00 503 1,600 0.24 * E‐W(2): 0.00
TH 1.00 191 1,600 0.12
LT 1.00 156 1,600 0.10 V/C: 0.76
Northbound RT 0.00 200 0 0.00 Lost Time: 0.10
TH 2.00 218 3,200 0.13
LT 1.00 69 1,600 0.04 *
Eastbound RT 1.00 303 1,600 0.15 * ICU: 0.86
TH 1.00 140 1,600 0.09
LT 1.00 53 1,600 0.03 LOS: D
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 145 0 0.00 N‐S(1): 0.19
TH 2.00 551 3,200 0.22 * N‐S(2): 0.27 *
LT 1.00 91 1,600 0.06 E‐W(1): 0.56 *
Westbound RT 1.00 741 1,600 0.41 * E‐W(2): 0.00
TH 1.00 304 1,600 0.19
LT 1.00 164 1,600 0.10 V/C: 0.83
Northbound RT 0.00 152 0 0.00 Lost Time: 0.10
TH 2.00 278 3,200 0.13
LT 1.00 73 1,600 0.05 *
Eastbound RT 1.00 305 1,600 0.15 * ICU: 0.93
TH 1.00 122 1,600 0.08
LT 1.00 61 1,600 0.04 LOS: E
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 2
Intersection: SR-154/US-101 SB On-Ramp
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : Y
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 0 0 0.00 N‐S(1): 0.50 *
TH 1.00 262 1,600 0.16 N‐S(2): 0.00
LT 2.00 961 2,720 0.35 * E‐W(1): 0.00 *
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.00 *
TH 0.00 0 0 0.00 *
LT 0.00 0 0 0.00 * V/C: 0.50
Northbound RT 1.00 212 1,600 0.13 Lost Time: 0.10
TH 2.00 487 3,200 0.15 *
LT 0.00 0 0 0.00
Eastbound RT 0.00 0 0 0.00 ICU: 0.60
TH 0.00 0 0 0.00 *
LT 0.00 0 0 0.00 * LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 0 0 0.00 N‐S(1): 0.46 *
TH 1.00 205 1,600 0.13 N‐S(2): 0.00
LT 2.00 815 2,720 0.30 * E‐W(1): 0.00 *
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.00 *
TH 0.00 0 0 0.00 *
LT 0.00 0 0 0.00 * V/C: 0.46
Northbound RT 1.00 234 1,600 0.15 Lost Time: 0.10
TH 2.00 503 3,200 0.16 *
LT 0.00 0 0 0.00
Eastbound RT 0.00 0 0 0.00 ICU: 0.56
TH 0.00 0 0 0.00 *
LT 0.00 0 0 0.00 * LOS: A
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 3
Intersection: Calle Real/State St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : Y
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 31 1,600 0.00 N‐S(1): 0.30 *
TH 0.00 0 0 0.00 N‐S(2): 0.00
LT 2.00 188 2,720 0.07 * E‐W(1): 0.17
Westbound RT 1.00 230 1,600 0.09 E‐W(2): 0.18 *
TH 3.00 775 4,800 0.16 *
LT 0.00 0 0 0.00 V/C: 0.48
Northbound RT 0.00 108 0 0.00 Lost Time: 0.10
TH 2.00 622 3,200 0.23 *
LT 1.00 205 1,600 0.13
Eastbound RT 0.00 0 0 0.00 ICU: 0.58
TH 2.00 534 3,200 0.17
LT 1.00 36 1,600 0.02 * LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 31 1,600 0.00 N‐S(1): 0.32 *
TH 0.00 0 0 0.00 N‐S(2): 0.00
LT 2.00 195 2,720 0.07 * E‐W(1): 0.19 *
Westbound RT 1.00 338 1,600 0.15 E‐W(2): 0.19 *
TH 3.00 824 4,800 0.17 *
LT 0.00 0 0 0.00 * V/C: 0.51
Northbound RT 0.00 117 0 0.00 Lost Time: 0.10
TH 2.00 676 3,200 0.25 *
LT 1.00 222 1,600 0.14
Eastbound RT 0.00 0 0 0.00 ICU: 0.61
TH 2.00 612 3,200 0.19 *
LT 1.00 31 1,600 0.02 * LOS: B
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 4
Intersection: La Cumbre Rd/State St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 105 0 0.00 N‐S(1): 0.19 *
TH 2.00 224 3,200 0.10 N‐S(2): 0.15
LT 1.00 128 1,600 0.08 * E‐W(1): 0.17
Westbound RT 1.00 58 1,600 0.00 E‐W(2): 0.20 *
TH 2.00 445 3,200 0.14 *
LT 1.00 87 1,600 0.05 V/C: 0.39
Northbound RT 1.00 89 1,600 0.00 Lost Time: 0.10
TH 1.00 169 1,600 0.11 *
LT 2.00 135 2,720 0.05
Eastbound RT 1.00 84 1,600 0.01 ICU: 0.49
TH 2.00 380 3,200 0.12
LT 1.00 97 1,600 0.06 * LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 76 0 0.00 N‐S(1): 0.23 *
TH 2.00 286 3,200 0.11 N‐S(2): 0.19
LT 1.00 165 1,600 0.10 * E‐W(1): 0.27
Westbound RT 1.00 119 1,600 0.00 E‐W(2): 0.29 *
TH 2.00 646 3,200 0.20 *
LT 1.00 183 1,600 0.11 V/C: 0.52
Northbound RT 1.00 166 1,600 0.00 Lost Time: 0.10
TH 1.00 202 1,600 0.13 *
LT 2.00 213 2,720 0.08
Eastbound RT 1.00 126 1,600 0.01 ICU: 0.62
TH 2.00 527 3,200 0.16
LT 1.00 139 1,600 0.09 * LOS: B
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 5
Intersection: Las Palmas Dr/La Cumbre Rd/Calle Real
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 0 0 0.00 N‐S(1): 0.10
TH 0.00 0 0 0.00 * N‐S(2): 0.16 *
LT 0.00 0 0 0.00 E‐W(1): 0.22 *
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.19
TH 2.00 604 3,200 0.19
LT 1.00 80 1,600 0.05 * V/C: 0.38
Northbound RT 1.61 342 2,573 0.10 Lost Time: 0.10
TH 0.00 0 0 0.00
LT 1.39 296 1,893 0.16 *
Eastbound RT 1.00 482 1,600 0.17 * ICU: 0.48
TH 2.00 494 3,200 0.15
LT 0.00 0 0 0.00 LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 0 0 0.00 N‐S(1): 0.12
TH 0.00 0 0 0.00 * N‐S(2): 0.19 *
LT 0.00 0 0 0.00 E‐W(1): 0.23 *
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.23 *
TH 2.00 727 3,200 0.23 *
LT 1.00 116 1,600 0.07 * V/C: 0.42
Northbound RT 1.72 437 2,749 0.12 Lost Time: 0.10
TH 0.00 0 0 0.00
LT 1.28 326 1,743 0.19 *
Eastbound RT 1.00 506 1,600 0.16 ICU: 0.52
TH 2.00 524 3,200 0.16 *
LT 0.00 0 0 0.00 * LOS: A
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 6
Intersection: US-101 SB Ramps/Las Palmas Dr
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 218 1,600 0.14 N‐S(1): 0.20 *
TH 0.05 14 81 0.17 N‐S(2): 0.17
LT 1.95 536 2,651 0.20 * E‐W(1): 0.31 *
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.27
TH 1.00 430 1,600 0.27
LT 2.00 470 2,720 0.17 * V/C: 0.51
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 0.00 0 0 0.00 *
LT 0.00 0 0 0.00
Eastbound RT 1.00 207 1,600 0.13 ICU: 0.61
TH 2.00 440 3,200 0.14 *
LT 0.00 0 0 0.00 LOS: B
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 153 1,600 0.10 N‐S(1): 0.24 *
TH 0.02 8 40 0.20 N‐S(2): 0.20
LT 1.98 633 2,686 0.24 * E‐W(1): 0.34 *
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.28
TH 1.00 453 1,600 0.28
LT 2.00 600 2,720 0.22 * V/C: 0.58
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 0.00 0 0 0.00 *
LT 0.00 0 0 0.00
Eastbound RT 1.00 138 1,600 0.09 ICU: 0.68
TH 2.00 397 3,200 0.12 *
LT 0.00 0 0 0.00 LOS: B
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 7
Intersection: Hope Ave/State St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 166 1,600 0.05 N‐S(1): 0.17 *
TH 1.00 174 1,600 0.11 N‐S(2): 0.14
LT 1.00 102 1,600 0.06 * E‐W(1): 0.23
Westbound RT 1.00 68 1,600 0.00 E‐W(2): 0.25 *
TH 2.00 610 3,200 0.19 *
LT 1.00 36 1,600 0.02 V/C: 0.42
Northbound RT 1.00 50 1,600 0.01 Lost Time: 0.10
TH 1.00 171 1,600 0.11 *
LT 1.00 50 1,600 0.03
Eastbound RT 0.00 41 0 0.00 ICU: 0.52
TH 2.00 631 3,200 0.21
LT 1.00 88 1,600 0.06 * LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 187 1,600 0.03 N‐S(1): 0.27 *
TH 1.00 154 1,600 0.10 N‐S(2): 0.14
LT 1.00 131 1,600 0.08 * E‐W(1): 0.32
Westbound RT 1.00 146 1,600 0.01 E‐W(2): 0.35 *
TH 2.00 825 3,200 0.26 *
LT 1.00 98 1,600 0.06 V/C: 0.62
Northbound RT 1.00 111 1,600 0.01 Lost Time: 0.10
TH 1.00 302 1,600 0.19 *
LT 1.00 58 1,600 0.04
Eastbound RT 0.00 52 0 0.00 ICU: 0.72
TH 2.00 790 3,200 0.26
LT 1.00 143 1,600 0.09 * LOS: C
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 8
Intersection: Hope Ave/Calle Real
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : Y
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 75 1,600 0.00 N‐S(1): 0.30 *
TH 1.00 85 1,600 0.05 * N‐S(2): 0.00
LT 1.00 58 1,600 0.04 E‐W(1): 0.20 *
Westbound RT 0.00 63 0 0.00 E‐W(2): 0.17
TH 2.00 161 3,200 0.07
LT 1.00 111 1,600 0.07 * V/C: 0.50
Northbound RT 1.00 123 1,600 0.01 Lost Time: 0.10
TH 0.71 240 1,139 0.21
LT 1.29 434 1,751 0.25 *
Eastbound RT 0.00 141 0 0.00 ICU: 0.60
TH 2.00 287 3,200 0.13 *
LT 1.00 155 1,600 0.10 LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 138 1,600 0.00 N‐S(1): 0.37 *
TH 1.00 124 1,594 0.08 N‐S(2): 0.00
LT 1.00 125 1,365 0.09 * E‐W(1): 0.26 *
Westbound RT 0.00 48 0 0.00 E‐W(2): 0.18
TH 2.00 178 3,200 0.07
LT 1.00 174 1,600 0.11 * V/C: 0.63
Northbound RT 1.00 140 1,600 0.00 Lost Time: 0.10
TH 0.72 271 1,144 0.24
LT 1.28 487 1,748 0.28 *
Eastbound RT 0.00 165 0 0.00 ICU: 0.73
TH 2.00 304 3,200 0.15 *
LT 1.00 180 1,600 0.11 LOS: C
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 9
Intersection: Hitchcock Way/State St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 3 0 0.00 N‐S(1): 0.06 *
TH 1.00 4 1,600 0.01 * N‐S(2): 0.06 *
LT 0.00 7 1,600 0.00 E‐W(1): 0.36 *
Westbound RT 0.00 11 0 0.00 E‐W(2): 0.21
TH 2.00 615 3,200 0.20
LT 1.00 176 1,600 0.11 * V/C: 0.42
Northbound RT 1.00 166 1,600 0.00 Lost Time: 0.10
TH 1.00 12 1,600 0.06
LT 0.00 77 1,600 0.05 *
Eastbound RT 0.00 86 0 0.00 ICU: 0.52
TH 2.00 726 3,200 0.25 *
LT 1.00 17 1,600 0.01 LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 12 0 0.00 N‐S(1): 0.11
TH 1.00 22 1,600 0.04 * N‐S(2): 0.12 *
LT 0.00 37 1,600 0.02 E‐W(1): 0.47 *
Westbound RT 0.00 21 0 0.00 E‐W(2): 0.32
TH 2.00 924 3,200 0.30
LT 1.00 238 1,600 0.15 * V/C: 0.59
Northbound RT 1.00 255 1,600 0.01 Lost Time: 0.10
TH 1.00 13 1,600 0.09
LT 0.00 128 1,600 0.08 *
Eastbound RT 0.00 98 0 0.00 ICU: 0.69
TH 2.00 911 3,200 0.32 *
LT 1.00 29 1,600 0.02 LOS: B
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 10
Intersection: Las Positas Rd/State St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 87 0 0.00 N‐S(1): 0.20 *
TH 2.00 241 3,200 0.10 N‐S(2): 0.17
LT 1.00 119 1,600 0.07 * E‐W(1): 0.31 *
Westbound RT 1.00 45 1,600 0.00 E‐W(2): 0.22
TH 2.00 572 3,200 0.18
LT 2.00 255 2,720 0.09 * V/C: 0.51
Northbound RT 1.00 335 1,600 0.13 Lost Time: 0.10
TH 1.00 211 1,600 0.13 *
LT 2.00 183 2,720 0.07
Eastbound RT 1.00 169 1,600 0.05 ICU: 0.61
TH 2.00 697 3,200 0.22 *
LT 1.00 64 1,600 0.04 LOS: B
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 61 0 0.00 N‐S(1): 0.21 *
TH 2.00 177 3,200 0.07 N‐S(2): 0.15
LT 1.00 83 1,600 0.05 * E‐W(1): 0.41 *
Westbound RT 1.00 94 1,600 0.01 E‐W(2): 0.34
TH 2.00 885 3,200 0.28
LT 2.00 337 2,720 0.12 * V/C: 0.62
Northbound RT 1.00 428 1,600 0.16 * Lost Time: 0.10
TH 1.00 211 1,600 0.13
LT 2.00 207 2,720 0.08
Eastbound RT 1.00 240 1,600 0.09 ICU: 0.72
TH 2.00 922 3,200 0.29 *
LT 1.00 90 1,600 0.06 LOS: C
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 11
Intersection: US-101 NB On-Ramp/Calle Real
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : Y
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 7 0 0.00 N‐S(1): 0.00
TH 1.00 11 1,600 0.02 * N‐S(2): 0.02 *
LT 0.00 7 1,600 0.00 E‐W(1): 0.75 *
Westbound RT 0.00 6 0 0.00 E‐W(2): 0.00
TH 1.00 263 1,600 0.17
LT 1.00 1,066 1,600 0.67 * V/C: 0.77
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 0.00 0 0 0.00
LT 0.00 0 0 0.00 *
Eastbound RT 0.00 6 0 0.00 ICU: 0.87
TH 2.00 231 3,200 0.08 *
LT 0.00 6 1,600 0.00 LOS: D
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 8 0 0.00 N‐S(1): 0.01 *
TH 1.00 0 1,600 0.01 * N‐S(2): 0.01 *
LT 0.00 8 1,600 0.01 * E‐W(1): 0.67 *
Westbound RT 0.00 14 0 0.00 E‐W(2): 0.00
TH 1.00 322 1,600 0.21
LT 1.00 866 1,600 0.54 * V/C: 0.68
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 0.00 0 0 0.00 *
LT 0.00 0 0 0.00 *
Eastbound RT 0.00 10 0 0.00 ICU: 0.78
TH 2.00 388 3,200 0.13 *
LT 0.00 7 1,600 0.00 LOS: C
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 12
Intersection: Las Positas Rd/Calle Real
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : Y
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 320 1,600 0.16 N‐S(1): 0.24
TH 2.00 591 3,200 0.19 * N‐S(2): 0.47 *
LT 0.00 1 1,600 0.00 E‐W(1): 0.26 *
Westbound RT 1.00 351 1,600 0.22 * E‐W(2): 0.00
TH 1.31 261 2,098 0.12
LT 1.69 336 2,296 0.15 V/C: 0.73
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 2.00 759 3,200 0.24
LT 2.00 766 2,720 0.28 *
Eastbound RT 1.00 174 1,600 0.00 ICU: 0.83
TH 0.00 0 0 0.00
LT 1.00 67 1,600 0.04 * LOS: D
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 284 1,600 0.13 N‐S(1): 0.25
TH 2.00 627 3,200 0.20 * N‐S(2): 0.35 *
LT 0.00 0 0 0.00 E‐W(1): 0.32 *
Westbound RT 1.00 403 1,600 0.25 * E‐W(2): 0.00
TH 1.52 476 2,438 0.20
LT 1.48 461 2,007 0.23 V/C: 0.67
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 2.00 801 3,200 0.25
LT 2.00 420 2,720 0.15 *
Eastbound RT 1.00 319 1,600 0.07 * ICU: 0.77
TH 0.00 0 0 0.00
LT 1.00 72 1,600 0.05 LOS: C
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 13
Intersection: Las Positas Rd/US-101 SB Ramps
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 0 0 0.00 N‐S(1): 0.54 *
TH 2.00 618 3,200 0.19 N‐S(2): 0.19
LT 2.00 483 2,720 0.18 * E‐W(1): 0.19
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.23 *
TH 0.00 0 0 0.00 *
LT 0.00 0 0 0.00 V/C: 0.77
Northbound RT 1.00 257 1,600 0.16 Lost Time: 0.10
TH 2.00 1,165 3,200 0.36 *
LT 0.00 1 1,600 0.00
Eastbound RT 0.00 302 0 0.00 ICU: 0.87
TH 1.00 1 1,600 0.19
LT 1.00 360 1,600 0.23 * LOS: D
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 0 0 0.00 N‐S(1): 0.44 *
TH 2.00 825 3,200 0.26 N‐S(2): 0.26
LT 2.00 582 2,720 0.21 * E‐W(1): 0.32 *
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.30
TH 0.00 0 0 0.00
LT 0.00 0 0 0.00 * V/C: 0.76
Northbound RT 1.00 215 1,600 0.13 Lost Time: 0.10
TH 2.00 734 3,200 0.23 *
LT 0.00 0 0 0.00
Eastbound RT 0.00 512 0 0.00 ICU: 0.86
TH 1.00 6 1,600 0.32 *
LT 1.00 487 1,600 0.30 LOS: D
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 14
Intersection: Las Positas Rd/Modoc Rd
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 222 0 0.00 N‐S(1): 0.36 *
TH 2.00 536 3,200 0.24 N‐S(2): 0.32
LT 1.00 162 1,600 0.10 * E‐W(1): 0.23
Westbound RT 0.00 360 0 0.00 E‐W(2): 0.47 *
TH 1.00 139 1,600 0.31 *
LT 1.00 31 1,600 0.02 V/C: 0.83
Northbound RT 0.00 37 0 0.00 Lost Time: 0.10
TH 2.00 810 3,200 0.26 *
LT 1.00 120 1,600 0.08
Eastbound RT 0.00 147 0 0.00 ICU: 0.93
TH 1.00 182 1,600 0.21
LT 1.00 253 1,600 0.16 * LOS: E
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 301 0 0.00 N‐S(1): 0.38
TH 2.00 734 3,200 0.32 * N‐S(2): 0.42 *
LT 1.00 302 1,600 0.19 E‐W(1): 0.23
Westbound RT 0.00 157 0 0.00 E‐W(2): 0.30 *
TH 1.00 105 1,600 0.16 *
LT 1.00 36 1,600 0.02 V/C: 0.72
Northbound RT 0.00 44 0 0.00 Lost Time: 0.10
TH 2.00 565 3,200 0.19
LT 1.00 152 1,600 0.10 *
Eastbound RT 0.00 145 0 0.00 ICU: 0.82
TH 1.00 189 1,600 0.21
LT 1.00 227 1,600 0.14 * LOS: D
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 15
Intersection: De La Vina St/State St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : Y
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 0 0 0.00 N‐S(1): 0.08 *
TH 0.00 0 0 0.00 N‐S(2): 0.00
LT 1.00 2 1,600 0.00 * E‐W(1): 0.35 *
Westbound RT 0.00 4 0 0.00 E‐W(2): 0.19
TH 2.00 613 3,200 0.19
LT 0.00 3 1,600 0.00 * V/C: 0.43
Northbound RT 0.00 18 0 0.00 Lost Time: 0.10
TH 0.17 0 269 0.07
LT 1.83 196 2,491 0.08 *
Eastbound RT 0.00 496 0 0.00 ICU: 0.53
TH 2.00 618 3,200 0.35 *
LT 0.00 4 1,600 0.00 LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.52 12 835 0.00 N‐S(1): 0.16 *
TH 0.00 0 0 0.00 N‐S(2): 0.00
LT 0.48 11 765 0.01 * E‐W(1): 0.45 *
Westbound RT 0.00 19 0 0.00 E‐W(2): 0.30
TH 2.00 916 3,200 0.29
LT 0.00 1 1,600 0.00 * V/C: 0.61
Northbound RT 0.00 25 0 0.00 Lost Time: 0.10
TH 0.13 2 213 0.13
LT 1.87 379 2,539 0.15 *
Eastbound RT 0.00 620 0 0.00 ICU: 0.71
TH 2.00 812 3,200 0.45 *
LT 0.00 9 1,600 0.01 LOS: C
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 16
Intersection: Alamar Ave/State St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 20 0 0.00 N‐S(1): 0.27 *
TH 1.00 224 1,600 0.15 N‐S(2): 0.20
LT 1.00 188 1,600 0.12 * E‐W(1): 0.20 *
Westbound RT 0.00 79 0 0.00 E‐W(2): 0.19
TH 2.00 430 3,200 0.16
LT 1.00 40 1,600 0.03 * V/C: 0.47
Northbound RT 0.00 54 0 0.00 Lost Time: 0.10
TH 1.00 182 1,600 0.15 *
LT 1.00 87 1,600 0.05
Eastbound RT 0.00 36 0 0.00 ICU: 0.57
TH 2.00 502 3,200 0.17 *
LT 1.00 47 1,600 0.03 LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 0.00 42 0 0.00 N‐S(1): 0.27 *
TH 1.00 185 1,600 0.14 N‐S(2): 0.23
LT 1.00 95 1,600 0.06 * E‐W(1): 0.27 *
Westbound RT 0.00 92 0 0.00 E‐W(2): 0.26
TH 2.00 635 3,200 0.23
LT 1.00 80 1,600 0.05 * V/C: 0.54
Northbound RT 0.00 98 0 0.00 Lost Time: 0.10
TH 1.00 238 1,600 0.21 *
LT 1.00 144 1,600 0.09
Eastbound RT 0.00 67 0 0.00 ICU: 0.64
TH 2.00 627 3,200 0.22 *
LT 1.00 42 1,600 0.03 LOS: B
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 18
Intersection: State St/Mission St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 61 1,600 0.01 N‐S(1): 0.13
TH 1.00 320 1,600 0.21 * N‐S(2): 0.27 *
LT 0.00 23 1,600 0.01 E‐W(1): 0.34 *
Westbound RT 0.00 30 0 0.00 E‐W(2): 0.29
TH 1.00 362 1,600 0.26
LT 0.00 17 1,600 0.01 * V/C: 0.61
Northbound RT 1.00 22 1,600 0.00 Lost Time: 0.10
TH 1.00 198 1,600 0.12
LT 1.00 99 1,600 0.06 *
Eastbound RT 0.00 131 0 0.00 ICU: 0.71
TH 1.00 360 1,600 0.33 *
LT 0.00 44 1,600 0.03 LOS: C
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 63 1,600 0.00 N‐S(1): 0.20
TH 1.00 347 1,600 0.23 * N‐S(2): 0.32 *
LT 0.00 21 1,600 0.01 E‐W(1): 0.36 *
Westbound RT 0.00 61 0 0.00 E‐W(2): 0.30
TH 1.00 342 1,600 0.26
LT 0.00 20 1,600 0.01 * V/C: 0.68
Northbound RT 1.00 23 1,600 0.00 Lost Time: 0.10
TH 1.00 300 1,600 0.19
LT 1.00 143 1,600 0.09 *
Eastbound RT 0.00 134 0 0.00 ICU: 0.78
TH 1.00 364 1,600 0.35 *
LT 0.00 56 1,600 0.04 LOS: C
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 19
Intersection: De La Vina St/Mission St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 291 1,600 0.18 * N‐S(1): 0.03
TH 2.00 473 3,200 0.16 N‐S(2): 0.18 *
LT 0.00 43 1,600 0.03 E‐W(1): 0.23 *
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.18
TH 2.00 545 3,200 0.18
LT 0.00 22 1,600 0.01 * V/C: 0.41
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 0.00 0 0 0.00
LT 0.00 0 0 0.00 *
Eastbound RT 1.00 353 1,600 0.22 * ICU: 0.51
TH 2.00 654 3,200 0.20
LT 0.00 0 0 0.00 LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 287 1,600 0.18 N‐S(1): 0.03
TH 2.00 620 3,200 0.21 * N‐S(2): 0.21 *
LT 0.00 53 1,600 0.03 E‐W(1): 0.22
Westbound RT 0.00 0 0 0.00 E‐W(2): 0.23 *
TH 2.00 687 3,200 0.23 *
LT 0.00 63 1,600 0.04 V/C: 0.44
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 0.00 0 0 0.00
LT 0.00 0 0 0.00 *
Eastbound RT 1.00 265 1,600 0.17 ICU: 0.54
TH 2.00 577 3,200 0.18
LT 0.00 0 0 0.00 * LOS: A
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 20
Intersection: Bath St/Mission St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : Y
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 88 1,600 0.00 N‐S(1): 0.14 *
TH 0.00 0 0 0.00 N‐S(2): 0.00
LT 1.00 43 1,600 0.03 * E‐W(1): 0.28
Westbound RT 0.00 43 0 0.00 E‐W(2): 0.30 *
TH 2.00 737 3,200 0.24 *
LT 0.00 0 0 0.00 V/C: 0.44
Northbound RT 1.00 44 1,600 0.03 Lost Time: 0.10
TH 1.00 134 1,600 0.08
LT 1.00 175 1,600 0.11 *
Eastbound RT 0.00 0 0 0.00 ICU: 0.54
TH 2.00 818 3,200 0.28
LT 0.00 88 1,600 0.06 * LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 161 1,600 0.06 * N‐S(1): 0.15 *
TH 0.00 0 0 0.00 N‐S(2): 0.00
LT 1.00 80 1,600 0.05 E‐W(1): 0.21
Westbound RT 0.00 44 0 0.00 E‐W(2): 0.32 *
TH 2.00 842 3,200 0.28 *
LT 0.00 0 0 0.00 V/C: 0.47
Northbound RT 1.00 31 1,600 0.02 Lost Time: 0.10
TH 1.00 135 1,600 0.08
LT 1.00 148 1,600 0.09 *
Eastbound RT 0.00 0 0 0.00 ICU: 0.57
TH 2.00 623 3,200 0.21
LT 0.00 60 1,600 0.04 * LOS: A
* = Critical Movement

ICU ANALYSIS

ICU ANALYSIS
ATTACHMENT 2
Project: Santa Barbara 2035 GPU
Int #: 21
Intersection: Castillo St/Mission St
Scenario: Existing Conditions
Thru Lane: 1600 vph N-S Split Phase : N
Left-Turn Lane: 1600 vph E-W Split Phase : N
Dual LT Penalty: 15 % Lost Time (% of cycle) : 10
Peak Period: AM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 167 1,600 0.03 N‐S(1): 0.01
TH 1.00 39 1,600 0.04 * N‐S(2): 0.04 *
LT 0.00 21 1,600 0.01 E‐W(1): 0.36
Westbound RT 0.00 24 0 0.00 E‐W(2): 0.39 *
TH 2.00 974 3,200 0.31 *
LT 0.00 7 1,600 0.00 V/C: 0.43
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 0.00 0 0 0.00
LT 0.00 0 0 0.00 *
Eastbound RT 0.00 187 0 0.00 ICU: 0.53
TH 2.00 828 3,200 0.36
LT 0.00 125 1,600 0.08 * LOS: A
Peak Period: PM PEAK HOUR
Approach Movement Lanes Volume Capacity V/C
Southbound RT 1.00 196 1,600 0.08 * N‐S(1): 0.01
TH 1.00 95 1,600 0.07 N‐S(2): 0.08 *
LT 0.00 18 1,600 0.01 E‐W(1): 0.35
Westbound RT 0.00 24 0 0.00 E‐W(2): 0.41 *
TH 2.00 1,133 3,200 0.37 *
LT 0.00 25 1,600 0.02 V/C: 0.49
Northbound RT 0.00 0 0 0.00 Lost Time: 0.10
TH 0.00 0 0 0.00
LT 0.00 0 0 0.00 *
Eastbound RT 0.00 295 0 0.00 ICU: 0.59
TH 2.00 692 3,200 0.33
LT 0.00 68 1,600 0.04 * LOS: A
* = Critical Movement

Santa Barbara City CouncilMarch 12, 2024

Santa Barbara City Council
March 12, 2024