(1995) 38 Cal.App.4th 1574, 1605 (new critical habitat regulation was not significant new information because impacts to the species had already been addressed in original EIR.).
The General Plan EIR’s discussion of VMT makes clear that VMT was a clearly understood metric for measuring air quality and GHG impacts at the time the EIR was certified. Thus, no new analysis of VMT to address the project’s impacts on transportation and traffic is required. However, to present a conservative analysis, the remainder of this memorandum presents the impacts of the project on VMT consistent with the State, County, and City requirements.
Existing Conditions
One performance measure used to quantify automobile travel is VMT, which refers to the amount of automobile travel attributable to a project as well as the distance traveled. In 2013, Governor Brown signed Senate Bill (SB) 743, which added Public Resources Code Section 21099 to the California Environmental Quality Act (CEQA). Public Resources Code Section 21099 changes the way transportation impacts are analyzed in CEQA documents, and aligns local environmental review methodologies with statewide objectives to reduce greenhouse gas (GHG) emissions, encourage infill mixed-use development in designated priority development areas, reduce regional sprawl, and reduce VMT in California.
Increased VMT leads to various direct and indirect impacts on the environment and human health.
Among other effects, increased VMT on the roadway network leads to increased emissions of air pollutants, including GHGs, and increased energy consumption. The transportation sector is associated with more GHG emissions than any other sector in California. As documented in the City’s Climate Action Plan (Richmond, 2016), about 39 percent of Richmond’s GHG emissions were produced by motor vehicles in 2012. Reducing VMT is one of the most effective means for reducing the City’s GHG emissions.
VMT is typically an output from travel demand models. Its calculation is based on the estimated number of vehicles multiplied by the distance traveled by each vehicle. This analysis uses the following VMT metrics:
x Household VMT per capita, which measures all the VMT by motor vehicle on a typical weekday associated with a residential use, such as trips to work, school, or shop, and divides that VMT by the number of residents in the project area. Househ old VMT is also referred to as home-based VMT.
x Commute VMT per worker, which measures all of the worker commute VMT by a motor vehicle on a typical weekday between homes and workplaces and divides that VMT by the Drew Finke and Karly Kaufman
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number of workers in the project area. Commute VMT is also referred to as home-work or home-based work VMT.
x Total VMT per service population, w hich mea sure s all of t he V MT of al l ve hi cle trips, vehicle types, and trip purposes for all of the project area, a nd divides that total VMT by the service population (i.e., sum of residents and workers) in the project area.
This analysis uses the Contra Costa Transportation Authority (CCTA) Countywide Travel Demand Model (CCTA Model) to estimate VMT. The CCTA Model includes data from 2020, which represents pre-pandemic conditions; therefore, the model approximates existing conditions. The VMT estimate accounts for all the VMT generated by the project area within the Nine County Bay Area region. Table 1 presents the existing VMT efficiency metrics (i.e., per capita, per worker, or per service population) for the project area and compares them to the citywide, countywide, and regionwide averages based on the CCTA Model results for the year 2020. The existing (2020) VMT efficiency metrics for the project area are lower that the citywide, countywide, and regionwide averages.
Table 1: Existing (2020) VMT Summary
Geography Average Household
VMT per Capita
Average Commute VMT
per Worker
Average Total VMT per
Service Population
Project Area 8.9 14.1 18.9
City of Richmond 10.4 15.5 23.0
Contra Costa County 17.3 14.9 30.0
Bay Area Region 13.3 15.6 26.2
Source: Fehr & Peers based on the results of the CCTA Travel Demand Model for the year 2020, 2022.
Regulatory Setting
The State, County, and City requirements regarding analysis of land use projects impact on VMT are discussed below.
Senate Bill 743
Passed in 2013, SB 743 started a process that changed the way transportation impact analysis is conducted as part of CEQA compliance. With these changes, automobile delay, level of service (LOS), and other similar measures of vehicular capacity or traffic congestion are no longer the basis for determining significant impacts under CEQA. According to SB 743, these changes are intended to “more appropriately balance the needs of congestion management with statewide Drew Finke and Karly Kaufman
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goals related to infill development, promotion of public health through active transportation, and reduction of greenhouse gas emissions.”
In December 2018, the Natural Resources Agency finalized updates to Section 15064.3 of the CEQA Guidelines, including the incorporation of SB 743 modifications. The Guidelines’ changes were approved by the Office of Administrative Law and as of July 1, 2020 are now in effect statewide.
To help aid lead agencies with SB 743 implementation, the Governor’s Office of Planning and Research (OPR) produced the Technical Advisory on Evaluating Transportation Impacts in CEQA that provides guidance about the variety of implementation questions they face with respect to shifting to a VMT metric.
Contra Costa Transportation Authority
Consistent with SB 743 requirements and OPR guidelines, in July 2020 CCTA published the VMT Analysis Methodology for Land Use Projects in Contra Costa GMTF Review Draft, to describe CCTA’s recommended methodology for evaluating VMT for land use projects in CEQA documents. The document provides thresholds of significance for projects of different types and a methodology for estimating VMT that jurisdictions in Contra Costa County are required to follow in order to be consistent with CCTA’s Growth Management Program (GMP).
City of Richmond
On February 4, 2021, the Richmond City Council adopted the VMT screening criteria, thresholds of significance, and VMT estimation methodologies as published by CCTA in July 2020 (described above), and consistent with SB 743 requirements and OPR guidelines.
On February 4, 2021, the Richmond City Council also adopted changes to the City’s Transportation Demand Management (TDM) Ordinance (Article 15.04.612 of the City of Richmond Municipal Code), which requires multi-unit residential developments of ten or more units and nonresidential developments of 10,000 or more square feet to develop and implement a TDM Plan to the extent feasible reduce the development’s vehicle trip generation to 15 percent below the standard rates as published in the latest edition of the Institute of Transportation Engineers’ Trip Generation Manual, and/or to ensure that the VMT per resident or worker for the development is below the citywide average.
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Impact Analysis
The methodology and assumptions used for evaluating the VMT impacts of the project as well as the VMT results are presented below.
VMT Estimation
This analysis uses the CCTA Model to estimate VMT efficiency metrics. Travel demand models represent neighborhoods in transportation analysis zones (TAZs). TAZs are used in transportation planning models for transportation analysis and other planning purposes. The CCTA Model includes approximately 138 TAZs in Richmond, which vary in size from a few blocks in the areas around the main corridors such as Cutting Boulevard and San Pablo Avenue and surrounding the Richmond BART Station to larger geographic areas farther away. The project area, which is defined as the TAZs were the proposed FBC would change land uses, fully or partially encompasses 48 TAZs.
The CCTA Model uses various socio-economic variables, such as number of households and residents by household type, number of jobs by employment category at a TAZ level and transportation system assumptions such as type of roadway, number of lanes, major bicycle and pedestrian facilities, transit service capacity and frequency to forecast various travel characteristics, such as daily and peak-hour travel volumes and VMT.
The CCTA Model uses a four-step modeling process that looks at trip generation, trip distribution, mode split, and trip assignment. This process accounts for changes in travel patterns due to future growth and expected changes in the transportation network. The CCTA Model assigns all predicted trips within, across, to, or from the nine-county San Francisco Bay Area region to the roadway network and transit system by mode (i.e., single-occupant or carpool vehicle, biking, walking, or transit) and transit carrier (i.e., bus, rail) for a given scenario. The VMT generated by each TAZ can be estimated by tracking the number of trips and the length of each trip generated by the TAZ.
The CCTA Model version released in April 2019, which incorporates land use data and transportation network improvements consistent with Plan Bay Area 2040 (i.e., the Sustainable Communities Strategy)1, was used to develop VMT estimates because it is currently the best 1 Although MTC adopted Plan Bay Area 2050 in October 2021, this analysis relies on Plan Bay Area 2040 because CCTA Model, which is used to estimate the VMT metrics for this project, is based on Plan Bay Area 2040 and updated to reflect Plan Bay Area 2050 are not expected for several years.
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available tool for estimating VMT metrics in Contra Costa County. VMT metrics are developed for the following scenarios:
x Baseline (2020) Conditions – This scenario represents the land uses and transportation network within and outside of the project area under existing (2020) conditions. x 2040 Plus Project Buildout Conditions – T h i s s c e n a r i o a s s u m e s t h e b u i l d o u t o f t h e proposed project within the project area, and the Plan Bay Area 2040 assumptions outside of the pr oject area. Regio nal planned t ransp orta tio n improveme nts include funded and approved transportation improvem ents as documented in the Plan Bay Area 2040 and included in the CCTA Travel Demand Model.
As a regional planning tool, the CCTA Model was developed through an extensive model validation process. The model is intended to replicate existing vehicular travel behavior, and can provide a reasonable estimate of VMT generated in various geographic areas on a typical weekday. It can also estimate future VMT that reflects planned local and regional land use and transportation system changes. As a result, the CCTA Model was used to estimate the VMT and the VMT efficiency metrics under 2020 and 2040 conditions.
The VMT and VMT efficiency metrics estimated by the CCTA Model are somewhat conservative because the CCTA Model is a regional travel demand model and only accounts for the built environment variables to which the model is sensitive. As such, the FBC policies supporting variables the model is not sensitive to (such as presence of bicycle and pedestrian facilities, limited parking supply, and TDM measures) may not be reflected in these VMT estimates.
Thresholds of Significance
Per City of Richmond’s adopted thresholds of significance for evaluating VMT impacts and consistent with OPR and CCTA guidelines, this analysis uses the following thresholds of significance to determine if the proposed project would cause a significant impact on VMT:
x For residential uses, the implementation of the proposed projec t would result in a significant impact if it would result in the average household VMT per capita to be higher than 85% of the existing average household VMT per capita in Richmond or Contra Costa County, whichever is less stringent.
x For employment generating uses, the implementation of the proposed project would result in a significant impact if it would result in the average commu te VMT per worker to be higher than 85% of the existing average commute VMT per worker in Richmond or Bay Area Region, whichever is less stringent.
x For retail and other uses, the implementation of the proposed p roject would result in a significant impact if it would result in the average total VMT per service population to be Drew Finke and Karly Kaufman
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higher than 85% of the existing average total VMT per service p opulation in Richmond or Contra Costa County, whichever is less stringent.
As previously shown in Table 1, the average household VMT per capita and the average total VMT per service population for Contra Costa County, and the average commute VMT per worker for the Bay Area region are the less stringent metrics and per the CCTA guidelines, are used as the thresholds of significance in this analysis.
Project VMT Metrics
Table 2 summarizes the VMT efficiency metrics listed above for the Baseline (2020) conditions in the project area and Project Buildout (2040) Conditions as estimated by the CCTA Model. The table also compares the VMT efficiency metrics for the project area with the appropriate baseline averages and a VMT 85 percent of the baseline averages (Countywide average for household VMT per capita and total VMT per service population, and regionwide average for commute VMT per worker), which are the threshold used to determine the significance of the VMT impact.
Table 2: Project VMT Metrics
Geography Average Household
VMT per Capita
Average Commute
VMT per Worker
Average Total VMT
per Service Population
Project Area
Baseline (2020) 8.9 14.1 18.9
Project Buildout (2040) 1 7.3 13.9 16.6
Baseline Average 2 17.3 15.6 30.0
85% of Baseline Average
(Threshold of Significance) 14.7 13.2 25.5
Above the threshold of
significance? No No 3 N o
Notes:
1. Buildout of the project, which represents an additional 5,151 residential units (16,483 residents) and 5,294 jobs (2,212,543 square feet of space).
2. Countywide average for household VMT per capita and total VMT per service population, and regionwide average for commute VMT per worker.
3. Based on the results of the CCTA Model, the average commute VMT per worker is estimated to be 13.8 under Project Buildout (2040) conditions. However, the CCTA Model does not account for TDM strategies that future developments are required to implement. It is estimated that TDM strategies would reduce the average commute VMT per worker by at least five percent to below the threshold of significance.
Source: Fehr & Peers based on the results of the CCTA Travel Demand Model, 2022.
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The effects of the project on the three VMT metrics is described below:
Household VMT per Capita
As shown in Table 2, the implementation of the proposed project would reduce the household VMT per capita in the project area from 8.9 under Baseline (2020) conditions to 7.3 under Project Buildout (2040) conditions, which would be below the threshold of significance of 14.7 (85 percent of the baseline countywide average), indicating a less-than-significant impact. The project would reduce the household VMT per capita in the project area because it would locate additional housing and increase the mix of uses in a transit-rich and pedestrian friendly area, which would encourage walking, biking, and transit trips and result in fewer and shorter automobile trips.
Commute VMT per Worker
The implementation of the proposed project would reduce the commute VMT per worker in the project area from 14.1 under Baseline (2020) conditions to 13.9 under Project Buildout (2040) conditions. Although the project would reduce the commute VMT per worker in the project area, it would remain above the threshold of significance of 13.2 (85 percent of the baseline Bay Area regional average) by about five percent, according to the CCTA Model. As described above, the VMT estimated by the CCTA Model only accounts for build-environment variables, and does not account for TDM measures that future developments are required to implement.
As stated above, the City has a TDM ordinance which requires multi-unit residential developments of ten or more units and nonresidential developments of 10,000 or more square feet to develop and implement a TDM Plan to reduce automobile trips and VMT to the extent feasible. Most future developments in the project area would be subject to this TDM ordinance. However, the specific TDM measures that future developments would implement are not known at this time.
Table 3 summarizes the potential VMT reducing strategies that future developments can implement to reduce the commute VMT per worker. Table 3 also presents the effectiveness range of each strategy based on research compiled in the Handbook for Analyzing Greenhouse Gas Emission Reductions, Assessing Climate Vulnerabilities, and Advancing Health and Equity, Designed for Local Governments, Communities, and Project Developers (California Air Pollution Control Officers Association [CAPCOA], December 2021). The CAPCOA report is a resource for local agencies to quantify the benefit, in terms of reduced travel demand and VMT, of implementing various TDM strategies.
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Furthermore, individual developments that are above the VMT threshold could potentially contribute to future VMT mitigation fee programs, banks, or exchanges. No regional VMT mitigation programs currently exist; however, the CCTA is currently evaluating different mitigation program frameworks which may lead to a Countywide or regional VMT mitigation program.
Should such a program be implemented, development projects could potentially pay into a fee program or purchase mitigation credits to achieve needed VMT mitigation instead of, or in addition to, onsite TDM measures.
Table 3: Summary of VMT Reduction Strategies
Strategy Description
Range of
Potential VMT
Reduction1
Implement ride-sharing
program
This strategy focuses on encouraging carpooling and vanpooling by project site/building tenants, which depends on the ultimate building tenants.
Up to 8%
Subsidize or discount
transit passes
This strategy reduces the need to use a vehicle by incentivizing individuals to use transit for their daily commute. This strategy depends on the ultimate building tenants.
Up to 5%
Price and manage
parking
Parking management strategies focus on the management of parking to influence vehicle travel. Free and ubiquitous parking supply tends to increase vehicle use while reducing parking supply and pricing spaces can help reduce vehicle travel. A reduction in parking supply, as required by the FBC, can incentivize infill development and higher density development by reducing the cost of building parking spaces.
Up to 20%
Mandatory Commute
trip reduction program
Implementing a mandatory Commute Trip Reduction (CTR)
program with rigorous monitoring requirements can disincentive single-occupancy vehicle trips and encourage alternative modes of transportation such as carpooling, taking transit, walking, and biking, thereby reducing VMT and GHG emissions.
Up to 26%
End-of-trip bicycle
facilities
This measure will install and maintain end-of-trip facilities for employee use. End-of-trip facilities include bike parking, bike lockers, showers, and personal lockers. The provision and maintenance of secure bike parking and related facilities encourages commuting by bicycle.
Up to 4%
Employer-sponsored
vanpool
Vanpooling is a flexible form of public transportation that provides groups of 5 to 15 people with a cost-effective and convenient rideshare option for commuting.
Up to 20%
Notes:
1. Based on Handbook for Analyzing Greenhouse Gas Emission Reductions, Assessing Climate Vulnerabilities, and Advancing Health and Equity, Designed for Local Governments, Communities, and Project Developers (CAPCOA, December 2021).
Source: Fehr & Peers, 2022.
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The effectiveness of TDM programs in reducing vehicle trips and VMT would depend on characteristics of individual developments and the specific TDM strategies implemented at each site. Although these details not known at this time, future development projects are expected to implement a suite of TDM measures that would reduce each project’s VMT to comply with the City’s TDM Ordinance.
Considering the effectiveness range of the TDM strategies described above and the City’s requirement for future developments to implement TDM plans, it is assumed that the implementation of the selected measures at future developments combined can reduce the average commute VMT per worker for the project area by at least five percent to below the threshold of significance (85 percent of the baseline Bay Area regional average). Thus, the impact of the proposed project on commute VMT per worker is less-than-significant.
Total VMT per Service Population
For total VMT per service population, the implementation of the proposed project would reduce the total VMT per service population from 18.9 under Baseline (2020) conditions to 16.6 under Project Buildout (2040) conditions, which would be below the threshold of significance of 25.5 (85 percent of the baseline countywide average), indicating a less-than-significant impact. The project would reduce the total VMT per service population in the project area because it would locate a variety of uses in a mixed-use, transit-rich, and pedestrian friendly area, which would encourage walking, biking, and transit trips and result in fewer and shorter automobile trips.
Cumulative VMT Analysis
According to the CCTA Guidance, a cumulative analysis is only required if the project-level impact is found to be significant. Since the project-level impact is less-than-significant, as described above, no cumulative analysis is prepared for the project.