Atherton City Council
November 20, 2024

Event/
Date City
Highway or Non-Highway Priority
Intersection?
Pedestrian, Bicycle Priority, or
Motor Vehicle concern? Specific Road/Intersection? Feedback Atherton General Comment Motor Vehicle Streets in Atherton are very dark and need better lighting.

Atherton General Comment Motor Vehicle Traffic and congestion are bigger issues in Atherton than safety.

Atherton Highway Priority Intersection Bicycle El Camino Real El Camino Real doesn't have enough space between cars and cyclists. Bike lanes aren't paved well so they encroach on drivers, and distracted drivers drift into the bike lane.

Atherton Highway Priority Intersection Bicycle El Camino Real On El Camino, cyclists bike against traffic, which is dangerous for pedestrians and motorists.

Atherton Highway Priority Intersection Bicycle El Camino Real Bike lanes on El Camino are too close to the cars.

Atherton Highway Priority Intersection Motor Vehicle El Camino Real Should make El Camino 2 lanes instead of 3 Atherton Highway Priority Intersection Bicycle El Camino Real Biking on El Camino is too difficult. There are too many fast cars, parked cars, cars pulling out, poor bike visibility. The bike signal on the intersection of El Camino and Atherton Ave. is very helpful.

Atherton Highway Priority Intersection Pedestrian El Camino Real There are flashing lights that pedestrians can push to cross El Camino, but cars often drive through them. Perhaps a more permanent stop sign, or light, would be safer.

Atherton Highway Priority Intersection Bicycle
El Camino Real south of
Atherton Ave.

The biggest problem for cyclists is El Camino Real, especially south of Atherton Ave. There is no shoulder for biking and to turn at intersections
Atherton Highway Priority Intersection Pedestrian
El Camino Real and Fair Oaks
Lane
People run stoplights at this intersection. It would also be nice to have a four-way crosswalk at this intersection, and directional paint on the streets to make it clearer how to navigate the intersection.

Atherton Highway Priority Intersection Motor Vehicle
El Camino Real and Alejandra
Ave.

It's hard to make a left turn from Alejandra Ave. onto El Camino Real to the point where it's easier to turn right and make a U-turn.

Atherton Highway Priority Intersection Motor Vehicle El Camino Real and Watkins Do not put a stoplight at this intersection.

Atherton Highway Priority Intersection Motor Vehicle El Camino Real and Maple Ave.

There are blind corners that need signage or mirrors to make visibility better, like at the intersection of El Camino and Maple Ave.

Atherton Highway Priority Intersection Motor Vehicle El Camino Real and Maple Ave.

This intersection is blind, especially turning right. There need to be mirrors so those turning can see oncoming traffic. Often drivers sit here for a very long time waiting for the turn to be safe, and at night it's very hard to see incoming motorists. It would be beneficial to have lights or stoplights here.

Atherton General Comment Pedestrian Need better crosswalks for strollers, especially getting on and off curbs.

Atherton Non-Highway Priority Intersection Motor Vehicle Maple Ave. and Dinkelspiel
Station Lane Cars go too fast on this intersection, there should be a pedestrian flashing lights sign here.

Atherton Non-Highway Priority Intersection Pedestrian
Maple Ave. and Dinkelspiel
Station Lane This intersection needs to be made safer for pedestrians, especially at night.

Atherton Non-Highway Priority Intersection Pedestrian
Fair Oaks Ln. intersection with
Caltrain tracks This road between Atherton Library and Caltrain tracks needs a crosswalk.

Atherton Non-Highway Priority Intersection Motor Vehicle Fair Oaks Lane and Dinkelspiel
Station Lane There is a blind intersection here that could be improved. Residents often complain about it.

Atherton Non-Highway Priority Intersection Motor Vehicle Fair Oaks Lane and Dinkelspiel
Station Lane This intersection is very dangerous; cars need better signage or paint to direct them.

Atherton Non-Highway Priority Intersection Motor Vehicle Fair Oaks Lane and Dinkelspiel
Station Lane
There is no stop sign turning left onto Dinkelspiel Station Lane from Fair Oaks Lane. There should be an incoming traffic sign here.

Atherton General Comment Bicycle
It's safer when cars aren't allowed to park on the corners of busy roads. This gives more visibility for cyclists at intersections.

Atherton Non-Highway Priority Intersection Bicycle
Valparaiso Ave. at Menlo
School
Atherton General Comment Bicycle
Motorists have a lot of close calls with bikes because there's not enough light at night to reflect off the bike reflectors.

Atherton Non-Highway Priority Intersection Bicycle Middlefield Rd. north of 9th Ave.

This section of Middlefield is not safe for cyclists, roads are narrow and cars backing out of parking spots.

Atherton Non-Highway Priority Intersection Bicycle Alameda de las Pulgas There are a lot of bicycle priority green dots on the map, but biking here is pretty safe because there is a bike lane and there are shoulders.

Atherton Non-Highway Priority Intersection Pedestrian
Isabella Ave. between El
Camino Real and Elena Ave.

Cars speed very fast down Isabella and the curve of the road creates blind turns and makes it very dangerous. We need mirrors to see around blind turns and to see approaching vehicles, as cyclists and pedestrians.

Atherton General Comment Pedestrian
Neighborhood east of
Middlefield Rd. There should be better pedestrian support in this area, including better sidewalks Atherton Non-Highway Priority Intersection Bicycle
Atherton Ave immediately West
of El Camino Real
There is a curve on Atherton Ave. here that has a curb in the middle of the road. This makes it hard for cars to pass cyclists.

Atherton Non-Highway Priority Intersection Motor Vehicle Selby Lane and Stockbridge
Ave. Turning left onto Stockbridge from Selby Lane is very dangerous.

Atherton Library, 1/16/24

Event/
Date City
Highway or Non-Highway Priority
Intersection?
Pedestrian, Bicycle Priority, or
Motor Vehicle concern? Specific Road/Intersection? Feedback Brisbane Non-Highway Priority Intersection Motor Vehicle Bayshore Blvd. and Tunnel
Ave. and Old County Road This intersection is dangers; fatalities happen here every year Brisbane Non-Highway Priority Intersection Motor Vehicle Bayshore Blvd. and San Bruno
Ave.

You can only make a right turn at this intersection. The block on left turns from Bayshore onto San Bruno should only be in effect during commuting hours. Otherwise it makes it so much longer to access the neighborhoods on San Bruno Ave.

Brisbane Non-Highway Priority Intersection Pedestrian
Tunnel Ave. near Bayshore
Blvd. and Tunnel Ave. and Old
County Road intersection There is no railing along the railroad tracks to protect pedestrians.

Brisbane Non-Highway Priority Intersection Pedestrian San Benito Rd.

Cars speed on this road and the cars parked along the windy road make it very dangerous.

This is also near a school and there is no sidewalks for children.

Brisbane General Comment Pedestrian Need to even out the sidewalks all over Brisbane Brisbane General Comment Bicycle
In general, it would be nice to have clearer bike lanes throughout Brisbane. Right now it's not clear where it's safe to bike.

Brisbane Non-Highway Priority Intersection Motor Vehicle Valley Dr. Valley Dr. needs better street lighting, it's really dark at night.

Brisbane Highway Priority Intersection Motor Vehicle 101 Exit to Bayshore Blvd. The right side of this exit is fenced off and cars go too fast, it's not safe Brisbane Non-Highway Priority Intersection Pedestrian
San Bruno Ave. and Mendocino
St.

People run through stop signs at this intersection frequently, and San Bruno Ave. is the walkway kids use to get to Brisbane Elementary School. Larger pedestrian walking signs are needed on San Bruno.

Brisbane Non-Highway Priority Intersection Bicycle Bayshore Blvd. Would like separated bike lanes on Bayshore to make it safer to cycle.

Brisbane Non-Highway Priority Intersection Motor Vehicle Glen Park Way between San
Bruno Ave. and Humboldt Rd.

Cars speed over 45 mph on this road going up the hill. Speed bumps and stop signs are needed along this road to curb speeding.

Brisbane Non-Highway Priority Intersection Pedestrian Mariposa St.

Going up Mariposa towards Brisbane Elementary there are cars parked along the road and there's low visibility. Cars park in the sidewalk and kids can't use the sidewalk to safely walk to school. This is very dangerous
Brisbane Non-Highway Priority Intersection Motor Vehicle Alvarado St. between Visitacion
Ave. and San Bruno Ave.

Cars go too fast down Alvarado and there is a preschool and elementary school on Alvarado.

Need speed bumps on this street
Brisbane Non-Highway Priority Intersection Motor Vehicle 5-way intersection (Old County
Rd., San Francisco Ave.,
Visitacion Ave., San Bruno
Ave.) This intersection is very poorly lit at night and visibility of the other cars needs to be improved.

Brisbane Non-Highway Priority Intersection Pedestrian San Benito Rd.

Cars speed down San Benito and there are no sidewalks for kids to walk down the super narrow street every morning to the elementary school. Need stop signs because this is a super curvey road with little visibility.

Brisbane Non-Highway Priority Intersection Bicycle Valley Dr.

Should take out a lane on each side, i.e. lane narrowing, to put in a safer bike lane. There isn't much traffic on this road so it won't be a problem to take out the lanes.

Brisbane Non-Highway Priority Intersection Motor Vehicle 5-way intersection (Old County
Rd., San Francisco Ave.,
Visitacion Ave., San Bruno
Ave.)
Coming down Visitacion towards this intersection, cars and trucks park outside of the hardware store and you can't see the oncoming traffic on San Bruno Ave. Shouldn't allow parking there, it's dangerous.

Brisbane Non-Highway Priority Intersection Pedestrian San Bruno Ave. Near Brisbane Elementary, it is so dangerous when cars speed and kids are walking to school.

Brisbane Non-Highway Priority Intersection Motor Vehicle Bayshore Blvd. to Thomas Ave. Route from Bayshore to Thomas is very badly lit and there's low visibility. Need street lighting Brisbane Non-Highway Priority Intersection Motor Vehicle West of San Bruno Ave. These roads are too curvy and there's low visibility Brisbane General Comment Bicycle
It would be great to have a bike path or walkway to make it safe walking South on Bayshore to get to the Bayfront. It's a beautiful walk but dangerous to even attempt now.

Brisbane Non-Highway Priority Intersection Motor Vehicle 5-way intersection (Old County
Rd., San Francisco Ave.,
Visitacion Ave., San Bruno
Ave.) No one stops at stop signs at this intersection. Should limit speed on Visitacion.

Brisbane Non-Highway Priority Intersection Motor Vehicle San Bruno Ave. and Santa
Clara St. Need to enforce stop sign at this intersection Brisbane Farmers Market, 1/18/24

Event/
Date City
Highway or Non-Highway Priority
Intersection?
Pedestrian, Bicycle Priority, or
Motor Vehicle concern? Specific Road/Intersection? Feedback Brisbane Non-Highway Priority Intersection Motor Vehicle 5-way intersection (Old County
Rd., San Francisco Ave.,
Visitacion Ave., San Bruno
Ave.)
Add a signal at this intersection; people almost get hit all the time and cars run through the stop sign. This is the most dangerous intersection in the city.

Brisbane Non-Highway Priority Intersection Motor Vehicle San Benito Rd. Cars drive too fast on this road and it's dangerous for other cars, pedestrians.

Brisbane Highway Priority Intersection Bicycle Bayshore Blvd. at 101 Exit The bike crossing at this exit is very dangerous Brisbane Non-Highway Priority Intersection Bicycle Bayshore Blvd.

There is no separated bike lane on Bayshore and it's easy to be hit by cars. Need a separate bike path here and physical barrier between bikes and the road.

Brisbane Highway Priority Intersection Motor Vehicle On stretch of 101 near Brisbane Trees cover signage along 101 near Brisbane and makes them hard/impossible to read.

Brisbane Non-Highway Priority Intersection Pedestrian
San Bruno Ave. near Brisbane
Elementary
The new drainage infrastructure on San Bruno around the elementary school is a tripping hazard, especially as it's in the way of kids walking to and from school.

Brisbane Non-Highway Priority Intersection Motor Vehicle Sierra Point East There needs to be more signage on Sierra Point East.

Brisbane Non-Highway Priority Intersection Pedestrian Sierra Point Rd.

The sidewalks on Sierra Point are really dangerous for pedestrians, especially East of Glen Park Way
Brisbane Non-Highway Priority Intersection Motor Vehicle 5-way intersection (Old County
Rd., San Francisco Ave.,
Visitacion Ave., San Bruno
Ave.) There should be a roundabout at this intersection to make it safer Brisbane Non-Highway Priority Intersection Motor Vehicle Sierra Point Rd. The road is very bumpy, needs resurfacing Brisbane Non-Highway Priority Intersection Motor Vehicle Bayshore Blvd. and Old County
Rd.

As you make a left from Bayshore onto Old County, there are very uneven road surfaces that need improvement
Brisbane Non-Highway Priority Intersection Bicycle Tunnel Rd. underpass The underpass on Tunnel Rd. needs a bike lane Brisbane Non-Highway Priority Intersection Motor Vehicle 5-way intersection (Old County
Rd., San Francisco Ave.,
Visitacion Ave., San Bruno
Ave.)
People don't stop at this intersection and it is so dangerous, especially for pedestrians. Need a camera here to capture those who don't stop and have flashing lights for pedestrians.

Brisbane Non-Highway Priority Intersection Motor Vehicle 5-way intersection (Old County
Rd., San Francisco Ave.,
Visitacion Ave., San Bruno
Ave.) This intersection is confusing and dangerous
Brisbane Non-Highway Priority Intersection Motor Vehicle San Bruno Ave. near Brisbane
Elementary We need a stoplight on San Bruno near Brisbane Elementary Brisbane Non-Highway Priority Intersection Motor Vehicle Tunnel Rd. underpass When crossing Tunnel Rd. at the bottom of the bridge underpass going towards South City, people blow past the stop sign and the stop sign gets covered by trees.

Brisbane Non-Highway Priority Intersection Pedestrian Kings Rd.

People drive too fast through the curvy, narrow road, especially in the morning. There are no sidewalks to safely walk as pedestrians.

Brisbane Non-Highway Priority Intersection Pedestrian Humboldt Rd. and Placer Way This intersection is so dangerous because it's curved and a blind corner. People don't stop at the stop sign and it's dangerouos for pedestrians.

Brisbane Non-Highway Priority Intersection Pedestrian
Visitacion Ave. and Monterey
St. At this intersection people don't stop at stop signs and many pedestrians are nearly hit here.

Brisbane General Comment General Comment Should lower speed limit on narrow roads, especially in the hills.

Brisbane Non-Highway Priority Intersection Pedestrian Bayshore Blvd. People drive too fast here, there are no crosswalks and it is not pedestrian friendly.

Brisbane Non-Highway Priority Intersection Pedestrian
Visitacion Ave. and Mariposa
St.

This is not an easily visible intersection for pedestrians crossing. Should have a 4-way stop here.

Brisbane Non-Highway Priority Intersection Motor Vehicle Tunnel Ave., Lagoon Rd. These roads are too narrow and very bouncy. There is a lot of traffic and a lot of biking.

Brisbane Non-Highway Priority Intersection Motor Vehicle Visitacion Ave. and Mariposa
St. Curbs in the middle of the road make turning very hard; should remove these.

Brisbane Non-Highway Priority Intersection Pedestrian Visitacion Ave. and Klamath St. There should be a diagonal intersection here for people to easily cross to the coffeeshop.

Brisbane Non-Highway Priority Intersection Motor Vehicle Sierra Point Rd. and San Benito
Rd.

Very dark at both intersections of San Benito and Sierra Point. The biggest problem is the western intersection.

Brisbane Non-Highway Priority Intersection Motor Vehicle San Bruno Ave. All stoplights need to be more visible along San Bruno, they are covered by trees.

Brisbane Non-Highway Priority Intersection Pedestrian
San Bruno Ave. near Brisbane
Elementary
Dangerous when people fly down San Bruno by the elementary school. We need crosswalks with buttom to turn on flashing lights and more signage.

Brisbane Highway Priority Intersection Bicycle
101 exit, Bayshore Blvd. and
Oyster Point
Navigating this intersection is super dangerous for bikers because cars are rushing to get onto the freeway and the bike lane totally disappears. There is no sidewalk or bikelane for this very high traffic area.

Brisbane Farmers Market, 1/18/24

/ City/County Association of Governments
San Mateo C/CAG Countywide LRSP
Appendix C
Crash Analysis Memo

Kittelson & Associates, Inc.

Technical Memorandum
February 5, 2024 Project# 28717
To: Jeff Lacap and Eva Gaye, C/CAG
From: Kittelson & Associates, Inc. and Safe Streets Research & Consulting, LLC RE: C/CAG LRSP – Final Task 3 Existing Conditions Analysis Memo

INTRODUCTION
This memo summarizes the methodology and key findings for hot spot and systemic crash analysis in San Mateo County as part of the C/CAG LRSP. Except where noted, the analysis was conducted countywide. The memo is organized as follows:
◼ Key Findings: Provides a high-level snapshot of systemic findings that can be used to identify priority locations countywide and within jurisdictions. ◼ Summary of Findings: Provides a more thorough description of findings, organized by travel mode (pedestrians, bicyclists, motor vehicles).
◼ Methodology: Describes how analysis data were obtained, processed, and analyzed ◼ Hot Spot Analysis: Uses crash data to develop mode-specific high injury networks ◼ Descriptive Systemic Analysis: Analyzes countywide crashes to assess crash risk associations among environmental, location, roadway geometry, behavioral, and demographic variables in available data.
◼ Jurisdiction-Specific Analysis: Supplements the countywide analysis by providing a perjurisdiction breakdown for the nine county jurisdictions without plans of top crash factors, roadway locations along the identified HINs, and intersections with the most frequent and severe crash history.
◼ Next Steps: Provides a summary of next steps for the project in relation to the findings in this memo.
155 Grand Avenue, Suite 505
Oakland, CA 94612
P 510.839.1742

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Final Task 3 Existing Conditions Analysis Memo Introduction Kittelson & Associates, Inc.

Contents
Introduction........................................................................................................................................................1 List of Tables....................................................................................................................................................4 List of Figures...................................................................................................................................................6 Key Findings........................................................................................................................................................7 Hot Spot Analysis............................................................................................................................................7 Systemic Analysis............................................................................................................................................7 Summary of Findings by Mode.....................................................................................................................11 Pedestrians.................................................................................................................................................11 Bicyclists......................................................................................................................................................13 Motor Vehicles..........................................................................................................................................15 Methodology ...................................................................................................................................................18 Hot Spot Analysis..........................................................................................................................................18 Sliding Window Analysis...........................................................................................................................18 High Injury Networks.................................................................................................................................19 Systemic Descriptive Analysis ....................................................................................................................19 Crash Data Overview .................................................................................................................................20 Roadway Data.............................................................................................................................................23 Quantifying Crash Severity.........................................................................................................................23 Data Preparation.........................................................................................................................................24 Roadway Functional Classification.......................................................................................................24 Coding Intersection versus Segment Crashes....................................................................................25 Injury Description ......................................................................................................................................25 Hot Spot Analysis Results ................................................................................................................................27 Pedestrian High Injury Network..................................................................................................................27 Bicyclist High Injury Network.......................................................................................................................31 Motor Vehicle High Injury Network ...........................................................................................................34 Combined High Injury Network.................................................................................................................37 Descriptive Analysis.........................................................................................................................................39 Crashes by Year ...........................................................................................................................................43 Environmental/Land Use ............................................................................................................................48 Lighting Conditions...................................................................................................................................48 Weather Conditions.................................................................................................................................51 Roadway Conditions...............................................................................................................................51

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Final Task 3 Existing Conditions Analysis Memo Introduction Kittelson & Associates, Inc.

Proximity to Transit Stop...........................................................................................................................51 Crash Locations............................................................................................................................................53 Intersection Crashes.................................................................................................................................56 Roadway Crash Geometric Characteristics..........................................................................................70 Functional Classification .........................................................................................................................70 Speed Limit ................................................................................................................................................74 Crash Dynamics/Behaviors........................................................................................................................77 Crash Type/Pre-Crash Movement Analysis .........................................................................................77 Violations....................................................................................................................................................83 Alcohol/Drug Usage ................................................................................................................................87 Solo Bicyclist Crashes ..................................................................................................................................89 Demographic Party and Victim Variables..............................................................................................92 Age..............................................................................................................................................................92 Sex ...............................................................................................................................................................99 Race..........................................................................................................................................................100 Jurisdiction-Specific Analysis.......................................................................................................................103 Belmont ....................................................................................................................................................105 Brisbane....................................................................................................................................................109 East Palo Alto...........................................................................................................................................113 Foster City.................................................................................................................................................118 Half Moon Bay.........................................................................................................................................122 Hillsborough .............................................................................................................................................126 Portola Valley ..........................................................................................................................................130 San Carlos................................................................................................................................................134 Woodside.................................................................................................................................................139 Next Steps.......................................................................................................................................................143 Appendix A Pedestrian HIN Maps by Jurisdiction
Appendix B Bicyclist HIN Maps by Jurisdiction
Appendix C Motor Vehicle HIN Maps by Jurisdiction
Appendix D Combined HIN Maps by Jurisdiction
Appendix E Countywide Combined HIN Locations

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Final Task 3 Existing Conditions Analysis Memo Introduction Kittelson & Associates, Inc.

List of Tables
Table 1: Most Common Intersection Functional Classes, All Modes (2018-2022).................................8 Table 2: Crash data overview, All Modes Aggregated (2018-2022).....................................................21 Table 3: Crashes by Jurisdiction, All Modes Aggregated (2018-2022)..................................................22 Table 4: Crash Weights by Severity and Location Type...........................................................................23 Table 5 Highest Pedestrian HIN Intersections Countywide......................................................................29 Table 6 Highest Bicyclist EPDO Intersections Countywide.......................................................................32 Table 7 Motor Vehicle HIN Intersections.....................................................................................................35 Table 8: Crashes by Year, All Modes Aggregated (2017-2022) .............................................................39 Table 9: AADT by Route (2017-2021)............................................................................................................40 Table 10: Crashes by Year and Jurisdiction, All Modes Aggregated (2017-2022)..............................41 Table 11: Crashes by Mode, Countywide (2018-2022)............................................................................42 Table 12: Crashes by Year, All Modes Aggregated (2018-2022) ...........................................................43 Table 13: Pedestrian Crashes by Year and Severity (2018-2022) ...........................................................45 Table 14: Bicyclist Crashes by Year and Severity (2018-2022) ................................................................46 Table 15: Motor Vehicle Crashes by Year and Severity...........................................................................47 Table 16: Lighting Conditions, Pedestrian Crashes (2018-2022).............................................................48 Table 17: Lighting Conditions, Bicyclist Crashes (2018-2022) ..................................................................49 Table 18: Lighting Conditions, Motor Vehicle Crashes (2018-2022).......................................................50 Table 19: Proximity to Transit Stop, Pedestrian Intersection Crashes (2018-2022) ...............................52 Table 20: Crash Locations, Pedestrian Crashes (2018-2022)...................................................................53 Table 21: Crash Locations, Bicyclist Crashes (2018-2022)........................................................................54 Table 22: Crash Locations, Motor Vehicle Crashes..................................................................................55 Table 23: Pedestrian Intersection Crashes by Functional Classification (2018-2022).........................57 Table 24: Bicyclist Intersection Crashes by Functional Classification (2018-2022)..............................58 Table 25: Motor Vehicle Intersection Crashes by Functional Classification.........................................59 Table 26: Intersection Signalization, Pedestrian Intersection Crashes (2018-2022).............................61 Table 27: Intersection Signalization, Bicyclist Intersection Crashes (2018-2022)..................................62 Table 28: Motor Vehicle Intersection Crashes Intersection Control ......................................................63 Table 29: Number of Legs, Pedestrian Intersection Crashes (2018-2022).............................................64 Table 30: Number of Legs, Bicyclist Intersection Crashes (2018-2022)..................................................65 Table 31: County Roadway Mileage by Posted Speed Limit .................................................................66 Table 32: Posted Speed Limit, Pedestrian Crashes (2018-2022) .............................................................67 Table 33: Posted Speed Limit, Bicycle Crashes (2018-2022)....................................................................68 Table 34: Motor Vehicle Intersection Crashes by Posted Speed Limit..................................................69 Table 35: Roadway Functional Class and Posted Speed Distribution, Countywide..........................70 Table 36: Functional Classification, Pedestrian Crashes (2018-2022)....................................................71 Table 37: Functional Classification, Bicycle Crashes (2018-2022)..........................................................72 Table 38: Functional Classification, Motor Vehicle Crashes (2018-2022) .............................................73 Table 39: Posted Speed Limit, Pedestrian Crashes (2018-2022) .............................................................74 Table 40: Posted Speed Limit, Bicycle Crashes (2018-2022)....................................................................75 Table 41: Speed Limit, Motor Vehicle Crashes ..........................................................................................76 Table 42: Movement-Based Crash Types, Pedestrian Crashes (2018-2022).........................................78 Table 43: Movement-Based Crash Types, Bicyclist Crashes (2018-2022)..............................................80 Table 44: Crash Type and Severity, Motor Vehicle Intersection Crashes.............................................81 Page 693 of 1188 February 5, 2024 Page 5
Final Task 3 Existing Conditions Analysis Memo Introduction Kittelson & Associates, Inc.

Table 45: Crash Type and Severity, Motor Vehicle Roadway Crashes.................................................82 Table 46: Reported Violations, Pedestrian Crashes (2018-2022)............................................................84 Table 47: Reported Violations, Bicyclist Crashes (2018-2022) .................................................................85 Table 48. Primary Collision Factors - Motor Vehicles.................................................................................86 Table 49: Under the Influence of Alcohol, Pedestrian Crashes (2018-2022)........................................87 Table 50: Under the Influence of Alcohol, Bicyclist Crashes (2018-2022).............................................88 Table 51: Alcohol/Drug Usage Crashes, Motor Vehicles.........................................................................88 Table 52: Solo Bicyclist Crashes (2018-2022)...............................................................................................89 Table 53: Victim age breakdown, Pedestrian crashes (2018-2022) ......................................................93 Table 54: Victim age breakdown, Bicyclist crashes (2018-2022)............................................................95 Table 55: Victim age breakdown, Motor Vehicle Only crashes (2018-2022) ......................................96 Table 56: Victim sex breakdown (2018-2022).............................................................................................99 Table 57: Fatally and severely injured victims by sex and mode (2018-2022)...................................100 Table 58: Victim racial breakdown (2018-2022)......................................................................................101 Table 59: Fatally and severely injured victims by race and mode (2018-2022)................................102 Table 60: Crashes by Severity, Mode, and Jurisdiction..........................................................................103 Table 61: Crash Statistics for Key Variables, Belmont .............................................................................105 Table 62: HIN Roadways in Belmont ..........................................................................................................108 Table 63: Belmont top EPDO intersections ...............................................................................................108 Table 64: Crash Statistics for Key Variables, Brisbane.............................................................................109 Table 65: HIN Roadways in Brisbane..........................................................................................................112 Table 66: Brisbane top EPDO intersections...............................................................................................112 Table 67: Crash Statistics for Key Variables, East Palo Alto ...................................................................113 Table 68: HIN Roadways in East Palo Alto ................................................................................................116 Table 69: East Palo Alto top EPDO intersections......................................................................................116 Table 70: Crash Statistics for Key Variables, Foster City .........................................................................118 Table 71: HIN Roadways in Foster City ......................................................................................................121 Table 72: Foster City top EPDO intersections............................................................................................121 Table 73: Crash Statistics for Key Variables, Half Moon Bay..................................................................122 Table 74: HIN Roadways in Half Moon Bay...............................................................................................125 Table 75: Half Moon Bay top EPDO intersections....................................................................................125 Table 76: Crash Statistics for Key Variables, Hillsborough ......................................................................126 Table 77: HIN Roadways in Hillsborough...................................................................................................129 Table 78: Hillsborough top EPDO intersections........................................................................................129 Table 79: Crash Statistics for Key Variables, Portola Valley ...................................................................130 Table 80: HIN Roadways in Portola Valley................................................................................................133 Table 81: Portola Valley top EPDO intersections.....................................................................................133 Table 82: Crash Statistics for Key Variables, San Carlos.........................................................................134 Table 83: HIN Roadways in San Carlos......................................................................................................137 Table 84: San Carlos top EPDO intersections...........................................................................................137 Table 85: Crash Statistics for Key Variables, Woodside..........................................................................139 Table 86: HIN Roadways in Woodside.......................................................................................................142 Table 87: Woodside top EPDO intersections............................................................................................142 Table 88: Countywide Combined HIN Roadways, sorted Alphabetically.........................................149

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Final Task 3 Existing Conditions Analysis Memo Introduction Kittelson & Associates, Inc.

List of Figures
Figure 1: Example of Sliding Window, Count of Crashes for Mile Segments........................................18 Figure 2 Pedestrian High Injury Network (HIN)............................................................................................30 Figure 3 Bicyclist High Injury Network (HIN).................................................................................................33 Figure 4 Motor Vehicle High Injury Network (HIN).....................................................................................36 Figure 5 Countywide High Injury Network (HIN).........................................................................................38 Figure 6: Annual EPDO Scores by Mode.....................................................................................................44

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Final Task 3 Existing Conditions Analysis Memo Key Findings Kittelson & Associates, Inc.

KEY FINDINGS
Hot Spot Analysis
The analysis identified high injury networks were developed for each travel mode, and top intersections were identified based on crash frequency and severity. These locations include state highways and local streets and will be used in subsequent project phases to identify project locations. Additionally, the characteristics of the HIN and crashes along them indicate risk factors that can be used to screen and identify priority locations.

All HINs, and the resulting combined HIN, can be viewed dynamically at this link. Static maps are contained in the body and appendix of this memo.
◼ The pedestrian HIN is represented by 98 miles of the street network that had the highest concentration of weighted crashes on a per mile basis. The minimum weighted crash threshold was substantially higher along state routes (i.e., Caltrans roadways) compared to local roads. This differential scoring was used to keep state routes from dominating the HIN and precluding insights about locally-controlled roadways. The Pedestrian HIN covers just

3.4 percent of the street network for the county but accounts for half of all pedestrian crashes and 69 percent of F/SI pedestrian crashes from 2018-2022. Roughly 12 percent of the pedestrian HIN is along a state route and 10 percent of the pedestrian HIN is along El Camino Real (SR 82).
◼ The bicyclist HIN was produced using the same approach as the pedestrian HIN (i.e., evaluating state routes separately from local streets and using the same relative scoring).

The bicyclist HIN represents 96 miles of the street network and accounts for 3.3 percent of the street network. Twenty-seven percent of the network is along a state route, with El Camino Real comprising nearly 10 percent of the bicyclist HIN mileage. Most crashes along the bicyclist HIN involved both a bicyclist and a motorist (82 percent of total crashes; 67 percent of F/SI crashes), while the remaining crashes were solo bicyclist crashes. ◼ The Motor Vehicle HIN was developed using the same sliding window analysis as the pedestrian and bicyclist HIN but used the same crash threshold for state highways and local roadways. The motor vehicle HIN represents 283 miles of the street network and accounts for about 10 percent of the street network, 63 percent of fatal/injury motor vehicle crashes, and 44 percent of F/SI motor vehicle crashes. Forty-two (42) percent of the network is along a state route.

Systemic Analysis
Similarities in Modal Findings
The following findings generally held true across modes:
◼ Crashes by year: Crashes reduced at the start of the pandemic (2020-21) but increased in severity during that time. Crash frequencies rebounded somewhat in 2022, consistent with traffic volumes picking back up and activities reopening.

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Final Task 3 Existing Conditions Analysis Memo Key Findings Kittelson & Associates, Inc.
◼ Lighting: Lighting conditions have a strong correlation with crash severity across modes.

Although most crashes occur in daylight, crashes occurring in dark conditions are more severe—especially in dark, unlit conditions.
◼ Crash Severity by Location: Across modes, segment and unsignalized intersection pedestrian crashes tended to be more severe than signalized intersections. This is likely explained by speed – for example, no intersection control along a major roadway unsignalized intersection. Higher speeds result in higher kinetic energy and limited reaction time, both of which may contribute to greater injury severity from these crashes. The perintersection crash rate was higher for signalized intersections across all modes, which makes sense given that signalized intersections are typically installed at locations that serve higher traffic volumes and that the vast majority of intersections in the County are unsignalized. ◼ Functional Classification: Primary and secondary roadways are overrepresented in crashes across modes although they only make up 3 and 7 percent of the roadway network by mileage. Residential streets account for relatively high shares of overall crashes but lower shares of F/SI crashes and markedly lower shares per roadway mile. ◼ Intersection Functional Classification: Crashes for all modes most frequently occurred at the intersection of higher order and lower order roadways. Four combinations dominate crash and F/SI crash frequency, shown below. Across all modes, intersections with primary roadways as the highest functional class have the highest rate of F/SI crashes per 100 intersections.

Table 1: Most Common Intersection Functional Classes, All Modes (2018-2022) Intersection
Functional Classes
% Share of Intersection Crashes (% Share of F/SI Crashes) Pedestrians Bicyclists Motor Vehicles
Secondaryresidential23 (25) 28 (24) 21 (18)
Primary-residential 20 (23) 17 (16) 24 (23)
Residentialresidential19 (13) 18 (13) 14 (!6)
Tertiary-residential 16 (13) 16 (18) 11 (12)
Source: TIMS, C/CAG, OSM 2023
◼ Speed: For all modes, roadways with 25 mph speed limits show relatively high shares of overall crashes but lower F/SI shares. Both shares are low compared to the proportion of the network they comprise (80 percent 25 mph and below). Higher speed roadways (45 mph and above) showed some of the highest shares of F/SI crashes and average equivalent property damage only (EPDO) scores (for more information on EPDO scores, see page 34). ◼ Age: Across all modes, victims between the ages of 15 and 34 are more likely to be injured including F/SI as result of traffic safety issues on average than other groups. Victims between the ages of 50 to 69 and 75 to 84 are more likely to be severely injured as a result of traffic

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safety issues on average than other groups. Research has found that an injury to a 65-yearold is approximately the same the injury a 30-year old would experience at 11 mph higher1. ◼ Sex: Males are more likely to be crash victims and are more likely to be injured than females.

They are killed or severely injured at a rate about 1.3 times higher than their share of the population.
◼ Race: Black and Hispanic travelers are victims in crashes at rates 2.2 time and 1.2 times their share of the population, respectively.

Differences in Modal Findings or Unique Modal Findings
◼ Proximity to Transit Stop: Intersections within 250 feet of a transit stop had much higher crash frequencies and rates per intersection than those farther away from transit stops, regardless of functional classification.
◼ Roadway Conditions: A low proportion of crashes (for all modes) occurred with any type of non-typical roadway condition, but 13 of the 17 such bicyclist e solo bicyclist crashes. This underscoring the vulnerability of bicyclists, the need for well-maintained roads, and perhaps the low likelihood of bicyclist crash reporting at lower severity levels. ◼ Location type: The most frequent locations vary by mode: pedestrian and bicyclist crashes most frequently occur at unsignalized intersections, but for motor vehicles the most frequent location is along roadway segments—which is where more F/SI bicyclist crashes occur than any other locations. To target trends systemically, agencies may consider different location types by mode.
◼ Intersection Number of Legs: Most pedestrian crashes and F/SI crashes occurred at fourlegged intersections, even though four-legged intersections represent only one-quarter of the network. However, most bicyclist crashes and bicyclist F/SI crashes occurred at threelegged intersections (though the per-intersection rate is lower than four-leg intersections).

For pedestrians the four-leg intersections may simply introduce more motor vehicle throughput, larger intersection size, and greater overall complexity. For bicyclists, there may be network/bicycle route reasons why three-leg intersections are the majority. A more robust roadway and intersection spatial data inventory could allow further insight into these trends to inform potential solutions.
◼ Crash types and pre-crash movements: The dynamics of crash types and pre-crash movements are meaningfully different across modes, both in terms of the data features that best describe trends and the most frequent movement patterns. High-level mode-specific findings are discussed in the Summary of Findings, and more detail is provided in relevant memo sections.
◼ Alcohol Usage: A smaller proportion of pedestrian and bicyclist crashes included a party under the influence of alcohol compared to motor vehicle crashes: four percent of pedestrian crashes, less than two percent of bicyclist crashes, and 10 percent of motor vehicles crashes. F/SI proportions were higher across the board, indicating that such crashes are more severe, but the numbers still differed by mode: six percent of F/SI pedestrian crashes, just under five percent of F/SI bicyclist crashes, and 19 percent of F/SI motor vehicle crashes.
◼ Sex: A higher share of males killed or severely injured in crashes are bicyclists (144 or 666, or 22 percent) than females (33 of 319, or9 percent), and a much higher share of bicyclists are male (144 or 177, or81 percent) than female (33 of 177, or19 percent). 1 Tefft, Brian C. 2013. Impact speed and a pedestrian's risk of severe injury or death. Accident Analysis & Prevention, 50: 871-878. https://doi.org/10.1016/j.aap.2012.07.022. Page 698 of 1188 February 5, 2024 Page 10
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◼ Race: Over half of bicyclist F/SI victims are white, higher than their 38 percent share of the population and commensurate 37 percent share of drivers fatally or severely injured. Thirty (30) percent of pedestrians fatally or severely injured were Hispanic, which is 1.25 times higher than their 24 percent share of the population.

Data Recommendations
The degree of systemic analysis findings possible is limited by data availability. In some cases, available findings point toward likely or possible causal factors, but without more detailed spatial data inventory we cannot draw conclusions more definitively. Some examples for potential future spatial data collection efforts include lane numbers, sidewalk inventory, presence and type of pedestrian crossing treatments, systemwide vehicle/pedestrian/bicycle volumes, and bicycle facility type/year constructed. This is by no means an exhaustive list but is indicative of the types of data that would enable further explanation of potential causal relationships indicated by this analysis. The Countywide LRSP may make data collection and inventory recommendations along these lines to further improve understanding of safety countywide.

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SUMMARY OF FINDINGS BY MODE
This section provides a brief overview of findings, listed by travel mode and by topic area. Some findings refer to overall EPDO score, which is a measure that combines crash frequency and severity by assigning weighted values to crashes and presenting a weighted total score. The measure is described in more detail in the Motor Vehicle High Injury Network section on page 34.

Pedestrians
Crashes by Year
◼ There was a sharp reduction in crashes at the start of the pandemic; however, crashes appear to have slightly increased in severity during the core pandemic years, from 18-20 percent of total EPDO scores in 2018-2019 to 22-23 percent of total EPDO scores in 2020- 2021.
◼ Pedestrian crash frequencies increased in 2022 as pandemic-related restrictions ease and travel/activities begin to increase, although the overall share of severe and fatal crashes dropped back to about 16 percent.

Lighting Conditions
◼ Lighting condition has a strong correlation with F/SI outcomes crashes that occurred in darkness or low-light (i.e., dusk or dawn) conditions were much more likely to result in a F/SI outcome (27 percent) compared to those that occurred during daylight (15 percent), and this disproportionality was even stronger in dark, unlit conditions (32 percent F/SI).

Proximity to Transit Stop
◼ Intersections within 250 feet of a transit stop had a crash rate of 20 crashes per 100 intersections, compared to just four crashes per 100 intersections for those intersections further away.

Crash Locations
◼ Most crashes occurred at an intersection (76 percent of all crashes; 68 percent of F/SI crashes), with the remainder coded to segment locations (24 percent of all crashes; 32 percent of F/SI crashes).
◼ Segment and unsignalized intersection pedestrian crashes tended to be more severe, with an average EPDO score of 51 and 40.5, respectively, compared to crashes at signalized intersections (average EPDO score of 30.5). Motorist speeds may be higher midblock than at intersections, resulting in higher kinetic energy and limited reaction time, both of which may contribute to greater injury severity from segment crashes.

Functional Classification
◼ Primary streets had the highest rate of crashes per 100 miles (340 crashes per 100 miles) and F/SI crashes (81 F/SI crashes per 100 miles), followed by secondary streets (137 crashes per 100 miles; 29 F/SI crashes per 100 miles). This finding supports research showing consistent pedestrian crash risk along major arterials, which generally have higher vehicle speeds, higher vehicle volumes, and greater crossing widths2.
2 Schneider, R.J., Sanders, R.L., Proulx, F.R., & Moayyed, H. (2021). United States Pedestrian Crash Hot Spot Locations and Characteristics. Journal of Transport & Land Use Page 700 of 1188, 14:1, 1-23. DOI: 10.5198/jtlu.2021.1825 February 5, 2024 Page 12
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Functional Classification - Intersection Locations
◼ Crashes occurred most frequently at
– secondary and residential streets (23 percent of all crashes; 28 percent of F/SI crashes), – primary and residential streets (20 percent of all crashes; 23 percent of F/SI crashes), and – residential and residential streets (19 percent of all crashes; 13 percent of F/SI crashes). ◼ Intersections between a primary and either another primary, a secondary, or a tertiary street had some of the highest rates of F/SI crashes and EPDO scores per 100 intersections.

Intersection Control
◼ Most intersection crashes (63 percent) and F/SI crashes (61 percent) occurred at unsignalized intersections.
◼ Crashes were concentrated at signalized intersections (51 crashes per 100 intersections; 9 F/SI crashes per 100 intersections).
◼ Crashes at signalized intersections most frequently involved:
– A pedestrian crossing in a crosswalk was struck by a left turning motorist (38 percent of all crashes; 29 percent of F/SI crashes).
– A pedestrian crossing in a crosswalk who was struck by a right turning motorist (23 percent of all crashes; 9 percent of F/SI crashes).
◼ Crashes that involved a pedestrian crossing in a crosswalk at a signalized intersection with a motorist going straight accounted for the second largest share of F/SI crashes (23 percent); these crashes were also disproportionately severe when they occurred, with 42 percent resulting in a F/SI outcome.

Number of Legs at Intersection
◼ Most pedestrian crashes (59 percent) and F/SI crashes (61 percent) occurred at four-legged intersections, even though four-legged intersections represent only one-quarter of the network. This finding likely reflects motor vehicle throughput, intersection size, and greater overall complexity.

Posted Speed Limit
◼ Roadways with 25-mph speed limits are underrepresented in F/SI crashes even though they accounted for the largest share of pedestrian crashes. They accounted for 38 percent of pedestrian crashes and 34 percent of F/SI crashes, but 80 percent of the street network has a speed limit of 25mph or less.
◼ Streets with a posted speed limit of 45 mph had the second largest share of crashes (27 percent) and F/SI crashes (29 percent), followed by 35-mph streets (23 percent of all crashes; 27 percent of F/SI crashes. These higher speed streets had some of the highest percentages of crashes resulting in an F/SI outcome, average EPDO scores, and EPDO scores per 100 miles. These findings support the current understanding that higher speed roads have serious safety implications for vulnerable road users.

Pre-Crash Movements
◼ Most pedestrian crashes involved:
– A pedestrian crossing at an intersection with a motorist turning left (24 percent). These crashes most frequently occurred at a signalized intersection (45 percent), but most of the F/SI crashes with this crash type occurred at unsignalized intersections.

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– A pedestrian crossing at an intersection with a motorist proceeding straight (15 percent).

These crashes most frequently occurred at unsignalized intersections for overall crashes (77 percent) and F/SI crashes (59 percent).
– A pedestrian crossing not in a crosswalk with a motorist proceeding straight (13 percent).

These crashes most often occurred at segment locations (46 percent of all crashes; 53 percent of F/SI crashes), followed by unsignalized intersections (39 percent of all crashes; 31 percent of F/SI crashes).
◼ Of those three crash types, those that involved a motorist proceeding straight had a higher share of crashes that resulted in a F/SI outcome and a higher average EPDO score.

Violation Types
The most common violations Included:
◼ pedestrian right of way3 (50 percent of all crashes; 37 percent of F/SI crashes) ◼ pedestrian violation (19 percent of all crashes; 30 percent of F/SI crashes) ◼ unknown (5 percent of all crashes; 10 percent of F/SI crashes) Alcohol Usage
◼ The vast majority of crashes did not have any road user found to be under the influence of alcohol (96 percent of all crashes; 94 percent of F/SI crashes). ◼ Despite the small sample size, crashes in which a pedestrian was reported to be under the influence of alcohol were much more likely to result in a F/SI .

Bicyclists
Crashes by Year
◼ Bicyclist crash frequencies were highest during the first two years of the five-year study period and lowest during the core period of the COVID-19 pandemic (2020-2021). ◼ Crash severity appears to be getting worse. Bicyclist injury trends showed that 20-23 percent of crashes resulted in a F/SI outcome in 2021-2022, compared to 11-15 percent of crashes in 2018-2020.

Lighting Conditions
◼ The vast majority of bicyclist crashes occurred during daylight conditions (84 percent), which fits with when most bicyclist trips occur.
◼ Bicyclist crashes were more likely to result in a fatal or severe injury outcome during dark lighting conditions (21 percent) compared to daylight conditions (16 percent).

Weather Conditions
◼ More than 90 percent of total crashes and F/SI bicyclist crashes occurred during clear weather conditions.

Roadway Conditions
◼ Most crashes occurred at locations with no unusual conditions (95 percent of crashes). ◼ Though a comparatively small sample, the majority of F/SI bicyclist crashes that occurred with any type of non-typical roadway condition (n=17) were solo bicyclist crashes (n=13), underscoring the vulnerability of bicyclists and the need for well-maintained roads. 3 See Appendix F for a definition of violation types. Page 702 of 1188 February 5, 2024 Page 14
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Crash Locations
◼ Most bicyclist crashes (43 percent) occurred at unsignalized intersections, but most F/SI crashes occurred at segment locations (43 percent). Roughly one-third of the F/SI crashes at unsignalized intersections were solo bicyclist crashes. ◼ Among the crashes at unsignalized intersections, and excluding solo bicyclist crashes, it was most common for both parties to be traveling in the same direction (e.g., an overtaking or dooring crash) for both overall crashes (45 percent) and F/SI crashes (58 percent). ◼ Among the unsignalized same-direction crashes, most involved both the bicyclist and motorist going straight (17 percent).
◼ Most F/SI crashes at unsignalized intersections involved both parties traveling in perpendicular directions, either both going straight (n=7) or the bicyclist going straight and the motorist making a left turn (n=4).

Functional Classification
◼ Most bicyclist crashes occurred along:
– secondary streets (39 percent),
– residential streets (20 percent), and
– primary streets (20 percent).
◼ Residential streets accounted for the second largest share of crashes and third largest share of F/SI crashes but had the lowest rates of crashes per 100 miles (11) and F/SI crashes per 100 miles (7) compared to primary streets (260 crashes per 100 miles; 224 F/SI crashes per 100 miles) and secondary streets (177 crashes per 100 miles; 128 F/SI crashes per 100 miles).

Functional Classification - Intersection Locations
◼ Crashes occurred most frequently at
– secondary-residential intersections (28 percent of all crashes; 24 percent of F/SI crashes), – residential-residential intersections (18 percent of all crashes; 13 percent of F/SI crashes), – primary-residential intersections (17 percent of all crashes; 16 percent of F/SI crashes), and
– tertiary-residential intersections (16 percent of all crashes; 18 percent of F/SI crashes). ◼ Crashes at intersections with only non-residential streets had a substantially higher rate of crashes and F/SI crashes per 100 intersections.

Intersection Control
◼ Most crashes occurred at unsignalized intersections, accounting for 67 percent of all crashes and 70 percent of F/SI crashes. When looking at crashes per 100 intersections, crashes were concentrated at signalized intersections (38 crashes per 100 intersections; 5 F/SI crashes per 100 intersections). The percentage of crashes that resulted in a F/SI outcome was comparable between location types, with 15 percent of crashes resulting in a F/SI outcome at unsignalized intersections compared to 13 percent at signalized intersections.

Number of Legs at Intersection
◼ Unlike pedestrian crashes, most bicyclist crashes (52 percent) and bicyclist F/SI crashes (55 percent) occurred at three-legged intersections. However, the three-legged locations still had a lower rate of crashes and F/SI crashes per 100 intersections compared to four-legged intersections.

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Posted Speed Limit
◼ Higher-speed streets tend to have very high EPDO scores per 100 miles. Most crashes and F/SI crashes occurred along 45-mph roadways (32 percent of all crashes; 31 percent of F/SI crashes), while only 8 percent of the network is made up of 45-mph streets. ◼ Streets with a posted speed limit of 40 mph had the largest rate of crashes per 100 miles (n=166) and F/SI crashes per 100 miles (n=138). Given that these streets comprise less than one percent of the network, these statistics indicate that a disproportionate share of overall crashes and F/SI crashes occur on these roads.

Pre-Crash Movements
◼ Most bicyclist crashes involved:
– Both roadway users proceeding straight at the time of the crash (21 percent of all crashes; 18 percent of F/SI crashes). These crashes occurred most frequently at unsignalized intersections (53 percent), whereas F/SI crashes occurred most frequently at signalized intersections (43 percent).
– A bicyclist proceeding straight and a motorist turning left (12 percent of all crashes; 9 percent of F/SI crashes). Most of these crashes occurred at unsignalized intersections (25 percent of all crashes; 46 percent of F/SI crashes).
– A bicyclist proceeding straight and a motorist turning right (11 percent of all crashes; 6 percent of F/SI crashes). Most of these crashes occurred at unsignalized intersections (43 percent of all crashes; 29 percent of F/SI crashes) and signalized intersections (32 percent of crashes; 53 percent of F/SI crashes).

Violation Types
The most frequent violation types included:
◼ automobile right of way (19 percent of crashes; 15 percent of F/SI crashes) ◼ unsafe speed (18 percent of crashes; 28 percent of F/SI crashes) ◼ improper turning (17 percent of crashes; 12 percent of F/SI crashes) Alcohol Usage
◼ Few reported crashes identified either the motorist or bicyclist as being under the influence of alcohol (less than two percent of all crashes; less than five percent of F/SI crashes).

Motor Vehicles
Crashes by Year
◼ Motor Vehicle crash frequencies were highest during the first two years of the five-year period and lowest during the core period of the COVID-19 pandemic (2020-2021). ◼ Crash severity has decreased, with the highest crash severity observed in 2018 which then decreased during the pandemic years (2020-2021).

Lighting Conditions
◼ About 26 percent of motor vehicle crashes were observed to have occurred in the dark.

Crashes with no streetlights had nearly double the per-crash EPDO score of daylight crashes.

Crash Locations
◼ About 39 percent of motor vehicle crashes and 52 of F/SI motor vehicle crashes occurred at a segment or mid-block location. Unsignalized intersection and roadway segment crashes are both more severe on average than signalized intersection crashes.

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Functional Classifications
◼ Secondary roadways are overrepresented in in F/SI share (36 percent) compared to total share (29 percent).
◼ Intersection crashes along primary and secondary roads account for the majority of intersection crashes (36 and 29 percent each) and similarly large shares of F/SI crashes (35 and 29 percent). The highest average EPDO scores were at intersections along the trunk and primary roadway system where they intersect with lower-order intersections. When looking at the signalized versus unsignalized breakdown of these combinations, the unsignalized intersections all have higher average EPDO scores—indicating that unsignalized minor-street intersections along arterial roadways have a disproportionate severity burden in the county.

Intersection Control
◼ The majority of motor vehicle crashes occurred at unsignalized intersections, but signalized intersections have a much higher crash rate per 100 intersections.

Posted Speed Limit
◼ Higher-speed streets tend to have very high EPDO scores. Most crashes and F/SI crashes occurred along roadways with speed greater than 40-mph roadways (56 percent of all crashes; 70 percent of F/SI crashes).
◼ Notably, almost 47 percent of the 40mph+ unsignalized intersection crashes occurred along secondary roadways A majority of crashes at 45 mph or higher intersections were at unsignalized intersections (56 percent)
◼ Roadways with posted speed limits of 40 mph or higher consist of 12 percent of County roadways but make up 44 percent total crashes and the majority (55 percent) of total F/SI crashes. Crashes that occurred on roadways with a posted speed limit of 40 mph or higher had an average EPDO per mile that was more than thirteen times that of crashes occurring on roadways with a posted speed limit of 25 mph or less.

Crash Types
◼ At intersections, the most frequent and severe motor vehicle crashes were the following types and locations:
– Broadside 61 percent occurred unsignalized intersections (66 percent of EPDO score) – Rear end 55 percent at occurred at signalized intersections (51 percent of EPDO score) – Head-on 51 percent at occurred at signalized intersections (59 percent of EPDO score) ◼ On roadway segments, the predominant crash types are rear end (29 percent), hit object (21 percent), and broadside (18 percent). Hit object crashes account for disproportionately large 35 percent of F/SI crashes and 30 percent of EPDO score, compared to 21 percent overall.

Violation Types
The most frequent violation types included:
◼ Unsafe Speed (27 percent of all crashes, 23 percent of F/SI crashes) ◼ Automobile right of way (19 percent of crashes; 12 percent of F/SI crashes) ◼ Improper turning (18 percent of crashes; 19 percent of F/SI crashes)

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Alcohol Usage
About 10 percent of the motor vehicle crashes included a driver under the influence, but those accounted for 19 percent of F/SI crashes and had an average EPDO about 1.5 times as high as other motor vehicle crashes.

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METHODOLOGY
This section outlines the elements related to two analyses that have been conducted: a hot spot analysis and a systemic crash analysis. The elements described in this section listed below are produced for the county as a whole and for the selected jurisdictions that do not have an existing safety plan.

Hot Spot Analysis
The hot spot analysis leveraged a sliding window approach to identify countywide HINs. Both are described in more detail below.

Sliding Window Analysis
Sliding Window Analysis is a segment-based approach that calculates crash frequency along a stretch of roadway (often weighted by injury severity). The sliding windows analysis consists of a virtual window of a predetermined length that is moved along the street network at predetermined step lengths and aggregates crashes that are within each window. One-mile window lengths and 1/10th-mile step increments were used in this analysis. Additionally, only fatal, severe injury, and minor injury crashes were included in the sliding window analysis and HIN development process. This decision was made to prioritize locations that have a history and high concentration of crashes that resulted in injury.

Figure 1 provides a graphical representation of the methodology. The figure shows segmentation (in purple) of a two-mile road using an analysis length of one-mile, with an analysis interval of a half-mile. Three segments are created. The crashes, shown in blue, are then associated with each segment, double-counting crashes as they overlap with one or more segments. Note that as the analysis interval decreases, the number of overlapping windows will increase if the analysis length remains constant, and vice-versa.

Figure 1: Example of Sliding Window, Count of Crashes for Mile Segments

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High Injury Networks
High injury networks are constructed by identifying a subset of the sliding window outputs. To produce an HIN, a minimum crash frequency or weighted aggregate value threshold is identified by reviewing the distribution of crashes for each mode along the HIN and the percentage of the network that is along the HIN.

HINs are a blend of art and science, needing to be large enough to be meaningful, but not so large as to be meaningless. This balance is even more pronounced for larger HINs, e.g., at the county level, that cover vastly different land use patterns and city sizes. To strike this balance, each mode-specific HIN is produced by an initial determination of a minimum threshold for the weighted crash value of segments to be included in the HIN, followed by review of the distribution of crashes for each mode along the relevant HIN and the percentage of the network that is along that HIN. If necessary, the threshold is adjusted to achieve the sought-after balance described above.

HINs can make for a useful communication tool because the data are reduced to a simple binary: streets on and off the HIN. At the same time, this data reduction masks variation, so the underlying granular sliding windows data may be more useful for internal prioritization procedures. Unlike intersection hot spot analysis, sliding windows analysis and HINs can identify entire corridors that have experienced patterns of crashes, leading to the possibility of systemic treatments.

Systemic Descriptive Analysis
A systemic descriptive analysis was conducted to identify patterns, trends, and potential risk factors based on five-year County crash data. The descriptive analysis identifies environmental, design, and user characteristics most associated with F/SI outcomes. The analysis includes the development of mode-specific crash types, which will further help develop a better understanding of the contributing factors which can lead to a more targeted and effective countermeasure selection process.

These findings will be used to define a set of Safe System-aligned recommendations, to target roadway or behavioral factors for those recommendations, and to provide prioritization and network screening inputs. Analyzing crashes at the countywide level provides sufficient number of observations to identify trends and allows for identification of issues that occur frequently across the network in similar locations but have may not occurred repeatedly at the same locations.

The list below includes common elements that have been included in a descriptive crash analysis. For each element, the frequency of overall crashes and fatal/severe injury (F/SI) crashes are analyzed to help identify factors that contribute to crashes for each mode. ◼ Crashes by Year
◼ Environmental/land use variables:
– Lighting condition Page 708 of 1188
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– Weather conditions
– Roadway conditions
– Proximity to transit stop
◼ Crash locations and roadway characteristics:
– Intersections versus roadway segment
– Intersection functional classification, control, number of legs, and speed limits – Roadway functional classification and speed limits
◼ Roadway variables, mid-block crashes:
– Functional classification
– Posted speed limit
◼ Crash dynamics/behaviors:
– Crash type analysis using the Location Movement Classification Method – Crash types and pre-crash movements for first two primary parties – Primary collision factors and violation types
– Alcohol/Drug usage
◼ Demographic, vehicle, and victim variables:
– Victims by age (compared to county population)
– Victims by sex (compared to county population)
– Victims by race (compared to county population)
Other variable relationships were explored but omitting from findings in cases where findings were not novel, unexpected, or relevant to future phases of the project. These analysis results are the basis for identifying countywide risk factors and emphasis areas.

Crash Data Overview
This section provides an overview of the five years of crash data used for this analysis. The data were downloaded from the Transportation Injury Mapping System4 (TIMS) Crash database representing the full years 2018 through 2022.

The crash, party and victim data tables were pulled from TIMS and processed for analysis. These tables have a relational structure, which is common for storing crash data. For every reported crash, there is one crash record. For every crash, the associated party table contains one entry for each primary actor in a crash: drivers, pedestrians, bicyclists, and parked vehicles involved.

For every party, an associated victim table includes one entry for each “victim”—which includes all people involved in the crash. For example, a car with a driver and two passengers would account for one party (the driver) but three victims (the driver plus the two passengers).

As described in the Task 2 methodology memo, this analysis includes only crashes for which some level of injury is reported and excludes property damage only (PDO) crashes. For motor vehicle-only crashes, PDO crashes are not predictive or descriptive of fatal or severe injury risk, so using injury crashes is sufficient and preferable. For pedestrian and bicyclist crashes, PDO crashes 4 Transportation Injury Mapping System, http://tims.berkeley.edu Page 709 of 1188 February 5, 2024 Page 21
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are rare and so infrequently reported as to add limited value to the analysis. Overall, PDO reporting practices vary from agency to agency and within agencies over time, so including PDOs may result in inconsistent frequencies or may bias findings based on reporting practices rather than true crash outcomes.

We removed crashes along grade-separated freeway were removed from the dataset, but we retained crashes that occur along at-grade State Highway facilities and those that occurred within the influence area of freeway ramp terminal intersections. The resulting dataset gave 8,468 injury and fatal crashes countywide.

Table 2 summarizes the total number of crashes that were observed Countywide, and Table 3 lists crashes that have occurred in each jurisdiction of the County along with the state highway versus local share of those crashes.

Countywide, 57 percent of reported crashes were on the state highway system. The following jurisdictions showed the highest variation from that share:
◼ Brisbane (contains no at-grade state highways): 100 percent local ◼ Portola Valley (SR 35 defines the southwestern boundary but otherwise contains no at-grade state highways): 100 percent local
◼ Hillsborough: 87 percent local
◼ Half Moon Bay: 23 percent local
◼ Unincorporated: 37 percent local
◼ Woodside: 39 percent local
Table 2: Crash data overview, All Modes Aggregated (2018-2022) Year Total Count Crashes
on nonfreeway
roadways
Crash Breakdown
State
Highway
Crashes
State
Highway
Share (%)
Local
Roadway
Crashes
Local
Roadway
Share
(%)
2018-
2022
12,526 8,468 2,712 43% 5,756 57%
Source: TIMS 2023

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Table 3: Crashes by Jurisdiction, All Modes Aggregated (2018-2022) Jurisdiction Count State
Highway
Crashes
% State
Highway
Crashes
Local
Roadway
Crashes
% Local
Roadway
Crashes
Atherton 121 36 30% 85 70%
Belmont 244 66 29% 178 71%
Brisbane 69 - 0% 69 100%
Burlingame 267 90 30% 177 70%
Colma 11 3 59% 8 41%
Daly City 863 143 29% 720 71%
East Palo Alto 458 150 33% 308 67%
Foster City 150 7 29% 143 71%
Half Moon Bay 145 116 77% 29 23%
Hillsborough 38 5 13% 33 87%
Menlo Park 542 188 33% 354 67%
Millbrae 230 94 38% 136 62%
Pacifica 394 141 56% 253 44%
Portola Valley 39 - 0% 39 100%
Redwood City 1,137 295 26% 842 74%
San Bruno 412 171 36% 241 64%
San Carlos 281 48 18% 233 82%
San Mateo 869 176 29% 693 71%
South San Francisco 866 168 17% 698 83%
Unincorporated 1,147 704 61% 443 39%
Woodside 185 111 61% 74 39%
Total 8,468 2,712 43% 5,756 57%
Source: TIMS 2023

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Roadway Data
The analysis was conducted using the county-provided roadway centerline spatial data obtained from the San Mateo County open data website.5 After careful review, we found that file to be the most complete available dataset. Even so, that dataset included the following limitations:
◼ Centerline data are missing in some locations, including for example along local roads where the data makes the roadway appear to be discontinuous even though in reality the route is continuous. However, this limitation did not prevent such roadways from being appropriately evaluated in the hot spot analysis or crashes being associated with the roadway along which they occurred.
◼ Some “paper streets” are present in the roadway file: roads that exist in data but do not exist as true roads in the network. We removed these where possible but may not have completely eliminated them given budgetary limitations on the ability to perform quality control reviews on the entire county’s roadway network. As a result, there are some intersections created from this roadway file that may not be “true” intersections. The results do not affect the HIN development but may result in some non-intersection crashes being coded as occurring at an intersection with a minor side street.

Quantifying Crash Severity
The analysis throughout this memo uses crash frequency and severity to identify findings. To include severity, we use the EPDO performance measure, which assigns weighting factors to crashes by severity relative to property damage only (PDO) crashes. The measure, referred to as “EPDO score,” assigns weight to individual crashes based on the crash severity and location of the crash. Weights may vary depending on agency data and priorities; in this analysis we use weights presented in the 2022 Caltrans’ Local Roadway Safety Manual and shown in Table 4.

These weights are based on California crash cost data assembled by Caltrans.

Table 4: Crash Weights by Severity and Location Type
Location Type
Crash Weighting by Severity
Fatal Severe
Injury Minor Injury Possible Injury
Signalized Intersection 120
11 6
Unsignalized Intersection 191
Roadway 165
Source: Caltrans, Local Roadway Safety: A manual for California’s Local Road Owners (Version 1.6), 2022.

The weights prioritize fatal and severe injury crashes equally to recognize that a death versus a severe injury is often a function of the individual involved or of emergency response time. 5 https://data-smcmaps.opendata.arcgis.com/ Page 712 of 1188 . Accessed May 1, 2023.

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Therefore, both outcomes represent locations where the region may want to prioritize improvements. Crash weights vary by location due to the relative costs associated with the crash severity at the location types. Specifically, unsignalized intersections have a higher cost for fatal and severe crashes because fatal and severe crashes at these locations tend to result in more severely injured persons on average. The EPDO score is calculated by multiplying each crash severity total by its associated weight and summing the results, using the following formula:

EPDO Score = Fatal weight * # of fatal crashes + severe injury weight * # of severe injury crashes + minor injury weight * # of minor injury crashes + possible in jury weight * # of possible injury weight crashes
The EPDO score is annualized by dividing the score by the number of years of crash data used in the analysis.

Data Preparation
To prepare the data for analysis, we proceeded with the following preprocessing steps.

Roadway Functional Classification
We associated crashes with the functional classification of the roadways on which they occurred or, for crashes at intersections, the combination thereof. Functional classification is a hierarchical understanding of the types of roads on a street network. Generally, higher functional classes have higher traffic volumes and speeds than lower functional class roads.

Functional classifications were taken from Open Street Map (OSM). These classifications in OSM are based on user-contributed data and may vary.

This analysis presents crash findings in relation to OSM-defined functional classifications because they provided more granularity than the county-provided roadway file. The following definitions and descriptions can be used to relate the values presented here back to the locally defined functional classifications:
◼ Trunk Roads: Trunk roads represent high speed arterials that can be partially separated.

These will typically be the state highway network and principal arterials. ◼ Primary Roads: Primary roads are smaller than trunk roads and represent interstate business routes and state routes in dense urban areas. These will be state highways that have a given street name as well as intracity arterial roadways.
◼ Secondary Roads: Secondary roads are generally smaller than primary roads and represent major urban streets. These will typically be collector roadways. ◼ Tertiary Roads: Tertiary roads are generally smaller than secondary roads and represent minor roads that connect local communities.
◼ Residential Roads: Residential roads similarly represent the local street network.

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Coding Intersection versus Segment Crashes
To facilitate analysis by location and the hot spot analysis, the team established variable intersection buffer definitions and coded all crashes to an intersection or, if not within any of the buffers described below, as a roadway segment (i.e., non-intersection) crash. ◼ A 250-foot buffer was used to define the influence area for the following intersections:
– trunk-trunk
– primary-primary
◼ A 150-foot buffer was used to define the influence area for the following intersections:
– trunk-secondary
– trunk-tertiary
– trunk-residential
– primary-secondary
– primary-tertiary
– primary-residential
◼ A 100-foot buffer was used to define the influence area for the following intersections:
– secondary-secondary
– secondary-tertiary
– secondary-residential
◼ A 75-foot buffer was used to define the influence area for the following intersections:
– tertiary-residential
– residential-residential
Injury Description
The officer-reported injury severity levels used in this analysis are specific to the most severely injured (MSI) party. Where the analysis reports all-modes crashes combined, the crash is coded by the highest level of severity for any party involved. However, the analysis primarily separates motor vehicle-only crashes, pedestrian-involved crashes, and bicyclist crashes. The San Francisco Department of Public Health (DPH) has documented reporting errors related to miscoded injury severities, particularly for severe injuries, suggesting a need for some fluidity when discussing minor and severe injuries.

For reference, the injury severities recorded in the crash data and summarized in this analysis are defined in the California Highway Patrol Collision Investigation Manual6:
◼ Fatal: A fatal injury is any injury that results in death within 30 days after the crash in which the injury occurred.
◼ Severe Injury: An injury, other than a fatal injury, that includes the following:
– Broken or fractured bones
– Dislocated or distorted limbs
6 HMP 110.5: Collision Investigation Manual. California Highway Patrol, February 2003, Pages 2 Page 714 of 1188 -10 to 2-11 February 5, 2024 Page 26
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– Severe lacerations
– Skull, spinal, chest or abdominal injuries that go beyond “other visible injuries” – Unconsciousness when taken from the crash scene
– Severe burns
◼ Minor Injury: A minor injury is any injury that is evident at the scene of the crash (also referred to as “other visible injuries”). Examples include:
– Bruises, discoloration, or swelling
– Minor lacerations or abrasions
– Minor burns
◼ Possible Injury: A possible injury is any injury reported or claimed which is not a fatal, suspected severe, or suspected minor injury (also referred to as “complaint of pain”).

Examples include people who:
– Seem dazed, confused, or incoherent.
– Are limping or complaining of pain or nausea
– May have been unconscious but appear to have recovered. – Exhibit momentary loss of consciousness, claim of injury, limping, or complaint of pain or nausea. Say they want to be listed as injured but do not appear to be so.

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HOT SPOT ANALYSIS RESULTS
The hot spot analysis consists of analyzing historic crash data for the county and within each local jurisdiction. Hot spot analysis is a valuable method to visualize locations with historic crash issues, but it does not prospectively identify locations with higher crash risk. To identify the modal High-Injury Networks (HINs) for pedestrians, bicyclists, and motor vehicles; a sliding window analysis was conducted as described in the Methodology section on page 18.

Pedestrian High Injury Network
The pedestrian HIN is displayed in Figure 2 and can be viewed dynamically online at this link7.

Static maps showing each jurisdiction are included in Appendix A.

The pedestrian HIN was developed using two separate weighted crash thresholds. State routes were analyzed separately due to the interjurisdictional coordination that is required to implement safety countermeasures along state routes, as well as the reality that the roadway characteristics present along state routes are not necessarily representative of local roads.

The pedestrian HIN used screening weights of three (for fatal and severe injury crashes) to one (for minor injury crashes). The pedestrian HIN is represented by 98 miles of the street network that had the highest concentration of weighted crashes on a per mile basis. The minimum weighted crash threshold was substantially higher along state routes (minimum score of 16) compared to local roads (minimum score of 6). This differential scoring was used to keep state routes from dominating the HIN and precluding insights about locally-controlled roadways. The Pedestrian HIN covers just 3.4 percent of the street network for the county, but accounts for half of all pedestrian crashes and 69 percent of F/SI pedestrian crashes from 2018-2022. Roughly 12 percent of the pedestrian HIN is along a state route and 10 percent of the pedestrian HIN is along El Camino Real.

Crashes along the pedestrian HIN shared a similar pattern with countywide crashes when exploring crashes by lighting condition. Most crashes occurred during daylight conditions (62 percent), which is expected given that most pedestrian trips occur during daylight hours.

However, most F/SI crashes occurred during dark lighting conditions (53 percent), when walking tends to be much less common. If we had temporally-adjusted pedestrian exposure, we would likely see that nighttime severe crash risk for pedestrians is even higher. Furthermore, crashes are nearly twice as likely to result in a F/SI outcome (27 percent) along the HIN during dark lighting conditions compared to daylight conditions (16 percent), underscoring pedestrian risk along this network.

Posted speed limit along the pedestrian HIN also appears to be associated with crash severity.

Roughly 30 percent of pedestrian crashes along 25mph HIN segments resulted in a F/SI outcome compared to 36 percent for 35mph streets. When looking at crashes by posted speed limit per 10 miles, HIN segments with a posted speed limit of 40 mph had the largest concentration of total pedestrian crashes (115 crashes per 10 miles; 9 F/SI crashes per 10 miles) followed by 30mph 7 https://kai.maps.arcgis.com/apps/instant/basic/index.html?appid=97ffc3b8dec94cf7a1d65ae3e3324de4 Page 716 of 1188 February 5, 2024 Page 28
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(74 crashes per 10 miles; 13 F/SI crashes per 10 miles) and 45 mph (54 crashes per 10 miles; 13 F/SI crashes per 10 miles).

The most frequent violation types include driver shall yield to pedestrian crossing the roadway at an intersection (60 percent of total crashes; 47 percent of F/SI crashes) followed by pedestrians shall yield right-of-way to vehicles except when at a marked crosswalk or an unmarked crosswalk at an intersection (10 percent of total crashes; 18 percent of F/SI crashes).

Crashes along the HIN occurred predominantly at intersections (85 percent of total crashes; 76 percent of F/SI crashes). However, crashes that occurred at segment locations were generally more severe with 44 percent of crashes resulting in a F/SI outcome, compared to 23 percent at intersection. This is consistent with the countywide analysis and is likely related to higher motor vehicle speeds and kinetic energy at the time of the crash. When looking at intersection signalization, unsignalized locations accounted for a larger share of pedestrian crashes and F/SI crashes (47 percent of total crashes; 50 percent of F/SI crashes) compared to signalized intersections (39 percent of total crashes; 30 percent of F/SI crashes), though signalized intersections had a much larger concentration of crashes due to there being fewer of them throughout the network.

Of the pedestrian crashes that occurred along the pedestrian HIN, the most common locationmovement crash type included the following:
◼ “intersection – pedestrian crossing in crosswalk, motorists making a left turn” (26 percent of total crashes; 21 percent of F/SI crashes),
◼ “intersection – pedestrian crossing in crosswalk, motorists proceeding straight” (20 percent of total crashes; 19 percent of F/SI crashes), and
◼ “intersection – pedestrian crossing in crosswalk, motorist making a right turn” (12 percent of total crashes; 6 percent of F/SI crashes).

Regardless of location type and pedestrian action at the time of the crash, crashes most frequently involved a motorist proceeding straight (40 percent of total crashes; 54 percent of F/SI crashes) followed by a motorist making a left turn (31 percent of total crashes; 28 percent of F/SI crashes). Crashes that involved a motorist going straight also tended to be more severe than crashes involving a motorist turning making a left turn or right turn: 37 percent of crashes involving a motorist going straight resulted in a F/SI outcome, and these crashes had the highest average EPDO score (67.3).

The Pedestrian HIN is largely concentrated near or within more urban areas of the county. This concentration is expected, as the generally higher volumes of pedestrian and motorist volumes in these areas lead to more pedestrian-motorist interactions and higher crash frequencies. The following communities accounted for the largest share of the pedestrian HIN by overall mileage:

South San Francisco (18 percent), San Mateo (18 percent), and Daly City (15 percent).

For informational purposes, the highest-scoring intersections in the county using EPDO scores for pedestrian crashes only are given in Table 5. The top three per jurisdiction are given in the Jurisdiction-Specific Analysis section beginning on page 103.

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Table 5 Highest Pedestrian HIN Intersections Countywide Intersection Name Jurisdiction State Highway Network? EPDO Score N Humboldt Street / E Poplar Avenue San Mateo No 606
E San Bruno Avenue / 3rd Avenue San Bruno No 410
El Camino Real / Center Street Millbrae Yes 393
W San Bruno Avenue / Masson Avenue San Bruno No 382
El Camino Real / Silva Avenue Millbrae Yes 371
El Camino Real / Chestnut Avenue South San
Francisco Yes 273
Westborough Boulevard / Galway
Drive
South San
Francisco No 240
Bayshore Boulevard / Guadalupe
Canyon Parkway Brisbane No 240
Mission Street / Evergreen Avenue Daly City No 230
Hillside Boulevard / Brunswick Street Daly City No 214

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Figure 2 Pedestrian High Injury Network (HIN)

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Bicyclist High Injury Network
The countywide bicyclist HIN can be found in Figure 3 and can be viewed dynamically online at this site8. Static maps showing each jurisdiction are included in Appendix B. The bicyclist HIN was produced using the same approach as the pedestrian HIN (i.e., evaluating state routes separately from local streets and using the same relative scoring). In this case, the minimum weighted bicyclist crash score per mile is six for both state routes and local streets, due to particularities of the data. The bicyclist HIN represents 96 miles of the street network and accounts for 3.3 percent of the street network. Twenty-seven percent of the network is along a state route, with El Camino Real comprising nearly 10 percent of the bicyclist HIN mileage. Most crashes along the bicyclist HIN involved both a bicyclist and a motorist (82 percent of total crashes; 67 percent of F/SI crashes), while the remaining crashes were solo bicyclist crashes.

Posted speed limit along the bicyclist HIN appears to be positively associated with bicyclist injury severity: the percentage of bicyclist crashes resulting in a F/SI outcome is higher along streets posted at 35mph or greater compared to streets posted at 30mph or lower. Interestingly, HIN segments with a posted speed limit of 45mph account for the largest share of HIN mileage (36 percent), followed by those posted at 35mph (27 percent) and 25mph (21 percent). HIN segments with a posted speed limit of 35mph had the largest share of bicyclist crashes (33 percent of total crashes; 39 percent of F/SI crashes), followed by 45mph segments (32 percent of total crashes; 32 percent of F/SI crashes). Streets with posted speed limits between 25mph and 35mph had comparable crash rates, ranging between 52 to 56 crashes per 10 miles. However, streets with a posted speed limit of 35mph had the highest rate of F/SI crashes per 10 miles (n=14), followed by 50mph (n=9) and 45mph (n=9).

Bicyclist crashes on the HIN occurred most frequently at intersections (64 percent of total crashes; 57 percent of F/SI crashes), and particularly at unsignalized intersections (43 percent of all crashes; 40 percent of F/SI crashes). However, crashes at segment locations were disproportionately severe (36 percent of all crashes; 43 percent of F/SI crashes). In terms of crash details, most bicyclist-motorist crashes included the roadway users traveling in perpendicular directions of travel (43 percent of crashes), followed by the same direction (34 percent of all crashes). Same direction crashes were disproportionately severe, comprising 41 percent of F/SI crashes. Unfortunately, roughly one-third of crashes and 41 percent of F/SI crashes do not have pre-crash movement data for both roadway users. Without these data, it is difficult to understand what types of behaviors may have contributed to these crashes. In the absence of these data, we can use the reported violation type, although this introduces a possible reporting bias or reporting errors and is a less objective attribute compared to using pre-crash movements.

With that caveat, failure to yield while entering highway was the most common violation type for perpendicular bicyclist crashes (13 percent of all crashes; 18 percent of F/SI crashes), followed by disregard red traffic signal (13 percent of all crashes; 25 percent of F/SI crashes) and drive on right except when passing (12 percent of all crashes; 2 percent of F/SI crashes). For same direction crashes, the most common violation types include inappropriate turn (28 percent of all crashes; 17 percent of F/SI crashes), followed by too fast for conditions (19 percent of all crashes; 25 percent of F/SI crashes).
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When looking at functional classification, secondary streets account for the largest share of the HIN in terms of network mileage (46 percent) followed by tertiary (21 percent) and primary (18 percent) streets. Crash rates per 10 miles are similar between residential streets (43 crashes per 10 miles), tertiary (47 crashes per 10 miles), and secondary (47 crashes per 10 miles) streets, with primary streets having the highest rate (54 crashes per 10 miles). However, primary and secondary streets had the highest rate of F/SI crashes per 10 miles, with 11 and 10 F/SI crashes per 10 miles, respectively. These types of streets generally have higher vehicle speeds, higher vehicle volumes, and are generally larger streets – all risk factors for F/SI crashes.

The Bicyclist HIN is largely concentrated near or within more urban areas of the county. This concentration is expected, as urban areas generally have higher volumes of bicyclist and motorist volumes, leading to more bicyclist-motorist interactions and higher crash frequencies.

However, roughly 15 percent of the bicyclist HIN is located in unincorporated areas of the county, a substantially higher amount than the corresponding part of the pedestrian HIN (roughly 2 percent). This finding likely reflects that bicyclists are able to travel longer distances than pedestrians, as well as a relatively high percentage of recreational bicycle trips in the county. The following communities had the largest share of the bicyclist HIN in terms of network mileage: Redwood City (16 percent), Menlo Park (10 percent), San Mateo (8 percent), and Woodside (8 percent).

For informational purposes, the highest-scoring intersections in the county using EPDO scores for bicyclist crashes only are given in Table 6. The top three per jurisdiction are given in the Jurisdiction-Specific Analysis section beginning on page 103.

Table 6 Highest Bicyclist EPDO Intersections Countywide Intersection Name Jurisdiction State Highway Network EPDO Score La Honda Road/ Portola Road Woodside Yes 404
El Camino Real / Selby Lane Atherton Yes 393
Manzanita Way / Sand Hill Road Unincorporated No 382
Donohoe Street / University Avenue East Palo Alto No 279 Portola Road / Sand Hill Road Unincorporated No 231
Alpine Road / Arastradero Road Portola Valley No 213
Alpine Road / Los Trancos Road Portola Valley No 202
Alameda De Las Pulgas / Howard Avenue San Carlos No 202 Alameda De Las Pulgas / Nassau Drive Unincorporated No 202 Skyline Boulevard / La Honda Road Woodside Y 202

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Figure 3 Bicyclist High Injury Network (HIN)

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Motor Vehicle High Injury Network
The countywide motor vehicle HIN can be found in Figure 4 and can be viewed dynamically online at this site9. Static maps showing each jurisdiction are included in Appendix C.

The Motor Vehicle HIN was developed using the same sliding window analysis as the pedestrian and bicyclist HIN but used the same crash threshold for state highways and local roadways and also used the EPDO scoring presented in the Methodology section on page 18. The minimum annualized EPDO score for inclusion on the motor vehicle HIN is 165 (representing, for example, one fatal roadway crash per year or 17 minor injury crashes per year), though where gaps were short identified along otherwise contiguous portions of roadways without underlying characteristic changes, they were connected.

The motor vehicle HIN represents 283 miles of the street network and accounts for about 10 percent of the street network. Forty-two (42) percent of the network is along a state route, and the roadways with the highest cumulative share of the HIN area include Skyline Boulevard, El Camino Real, and State Route 1 (cumulatively 24 percent of the HIN). Roadways in unincorporated areas of the county comprise about 27 percent of the HIN.

As with motor vehicle crashes overall, higher posted speed limit correlates crash outcomes: 50 percent of crashes on the HIN are along 45+ mph roadways (58 percent of F/SI crashes). The crashes that occurred at higher speeds along the HIN are primarily along roadway segments (60 percent) and include the following as the most frequent and severe crash types on these highspeed roadways:
◼ Segment hit object crashes (18 percent of crashes, 22 percent of F/SI) ◼ Segment head-on crashes (11 percent of crashes, 15 percent of F/SI) ◼ Unsignalized intersection broadside crashes (9 percent of crashes 8 percent of F/SI) The majority of crashes on the motor vehicle HIN occur along primary and secondary roadways (39 percent each) along with F/SI crashes (25 percent primary and 50 percent secondary). The most frequent intersection combinations represented among HIN crashes were:
◼ Primary-residential roadways (22 percent of crashes and 15 percent of F/SI crashes), which were primarily signalized.
◼ Secondary-residential roadways (14 percent of crashes and 9 percent of F/SI crashes), which were majority unsignalized.

Among violations, three violations dominated:
◼ Unsafe speed (30 percent of crashes, 23 percent of F/SI), distributed among location types ◼ Automobile right of way (18 percent of crashes and of F/SI), primarily at unsignalized intersections (55 percent)
◼ Improper turning (15 percent of crashes and 13 percent of F/SI), primarily on roadway segments (46 percent) indicating access points as possible causal factor. 9 https://kai.maps.arcgis.com/apps/instant/basic/index.html?appid=97ffc3b8dec94cf7a1d65ae3e3324de4 Page 723 of 1188 February 5, 2024 Page 35
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For informational purposes, the highest-scoring intersections in the county using EPDO scores for motor vehicle crashes only are given in Table 7. The top three per jurisdiction are given in the Jurisdiction-Specific Analysis section beginning on page 103.

Table 7 Motor Vehicle HIN Intersections
Intersection Name Jurisdiction State Highway
Network?
EPDO Score
Half Moon Bay Road / Old Cañada
Drive Unincorporated Yes 612
Skyline Boulevard / Alpine Road Unincorporated Yes 573
Linden Avenue / Baden Avenue South San
Francisco
No 421
University Avenue / Weeks Street East Palo Alto Yes 404 Skyline Boulevard / John Daly Boulevard Daly City Yes 402 Woodside Road / Middlefield Road Redwood City Yes 396
Cabrillo Highway / Pescadero Creek
Road Unincorporated Yes 388
Edgewood Road / Junipero Serra
Freeway Unincorporated Yes 382
Woodside Road / Broadway Street Redwood City Yes 372
Whipple Avenue / Veterans Boulevard Redwood City No 371

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Figure 4 Motor Vehicle High Injury Network (HIN)

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Combined High Injury Network
For the purpose of identifying a consolidated network of high injury segments for the San Mateo County and specific jurisdictions, motor vehicle HIN, pedestrian HIN and bicycle HIN were combined to identify, for every roadway segment in the County, whether it is part of zero, one, two, or all three of the modal HINs.

The countywide combined HIN can be found in Figure 5 and can be viewed dynamically online at this site10. Static maps showing each jurisdiction are included in Appendix D, and a complete table showing every roadway along any of the modal HINs is included in Appendix E.

In subsequent project tasks, these HIN scores (zero through three) may be combined and weighted to identify priority locations per mode or overall. 10 https://kai.maps.arcgis.com/apps/instant/basic/index.html?appid=97ffc3b8dec94cf7a1d65ae3e3324de4 Page 726 of 1188 February 5, 2024 Page 38
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Figure 5 Countywide High Injury Network (HIN)

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DESCRIPTIVE ANALYSIS
This section includes descriptive analysis findings that form the basis for systemic safety findings.

The analysis starts with a pre-pandemic vs pandemic comparison to understand the differences in safety, considering the significant change of travel behaviors that occurred during the pandemic. It is then followed by an in-depth analysis of crashes that have occurred from the year 2018-2022, conducted by modes including pedestrian, bicyclists, and motor vehicles. Crash locations for both intersection and mid-block crashes have been analyzed. Lastly, characteristics of individuals involved in crashes has been observed to account for the age, sex, and race.

Pre-Pandemic versus Pandemic Comparison
The COVID-19 pandemic has had a significant effect on travel patterns, behaviors, and traffic volumes. The analysis considered crashes that occurred before the pandemic (2017-2019) and during the pandemic years (2020-2022) to observe changes in safety due to the COVID-19 pandemic.

Based on the findings as listed in Table 8, the total number of crashes have decreased, but the number of F/SI crashes before versus after is quite similar (52 compared to 48 percent of the sixyear total). This is also evident in the EPDO score per crash which indicates that in the pandemic era the average severity has been about 20 percent higher compared to 2017-2019.

Table 8: Crashes by Year, All Modes Aggregated (2017-2022) Year Count % of 6-year
Total Crashes
F/SI
Count
% of Total
F/SI Crashes
EPDO
Score
% of Total
EPDO
Score
Average
EPDO Score
2017-
2019
6,109 58% 555 52% 139,258 54% 22.8
2020-
2022
4,436 42% 518 48% 120,774 46% 27.2
Total 10,545 100% 1,073 100% 260,032 100% 24.7
Source: TIMS 2023
Based on the findings as listed in Table 9, traffic volumes have decreased along state highway routes in the San Mateo County area with the exception of the Route 35 San Mateo County and Santa Clara County line traffic census location which has stayed the same from 2017 to 2021.

Largest reductions in traffic volume were recorded along Route 82 in Colma and Daly City where traffic volumes have decreased by 37.9% from 2017 to 2021.

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Table 9: AADT by Route (2017-2021)
Route Description 2017 2018 2019 2020 2021
%
Change
from
2017 to
2021
1 Santa Cruz/San Mateo
County Line 5,400 5,400 5,000 3,900 3,900 -28%
1 Jct. Rte. 84 East 7,600 7,600 7,600 5,900 6,000 -21%
1 San Mateo/San
Francisco County Line 90,300 90,300 101,000 79,000 80,000 -11% 35 Jct. Rte. 92 3,400 3,400 3,400 3,300 3,300 -3%
35 John Daly Boulevard 32,000 32,000 32,000 30,000 30,500 -5% 82 Jct. Rte. 84 39,400 36,100 36,100 28,500 31,000 -21% 82 San Mateo, Jct. Rte. 92 47,300 42,200 42,200 33,000 36,000 -24% 82 Colma, Mission Road 31,000 31,600 31,600 24,800 27,000 -13% 82 Daly City, Mission Street 15,300 10,600 10,600 8,300 9,500 -38% 82 San Mateo/San
Francisco County Line 15,300 10,600 10,600 8,300 9,500 -38% 84 Pescadero Road 2,200 2,300 2,300 2,000 2,100 -5%
84 Redwood City, Jct. Rte.
82 51,000 47,000 47,000 40,000 40,500 -21%
92 Jct. Rte. 35 South 28,900 29,500 29,500 23,000 23,200 -20% 92 Jct. Rte. 82 109,200 93,000 93,000 72,000 88,000 -19% 101 San Mateo, Jct. Rte. 92 280,400 272,800 267,000 214,000 203,000 -28% 101 San Francisco/San
Mateo County Line 226,800 220,600 219,000 175,000 166000 -27% 280 Junipero Sierra 226,000 216,700 216,700 175,000 162000 -28% 380 South San Francisco, Jct.

Rte. 101 23,700 23,700 23,000 19,300 19,500 -18%
Source: Caltrans Traffic Census Program
Based on the findings as listed in Table 10, the total number of crashes have decreased from pre-pandemic to pandemic era in most jurisdictions with the exception of Broadmoor, East Palo Alto, Half Moon Bay, and Woodside. The cities of Atherton, Broadmoor, Burlingame, Daly City, East Palo Alto, Foster City, Millbrae, Pacifica, Portola Valley, Redwood City, San Carlos, Woodside, and Unincorporated San Mateo County (64 percent of total jurisdictions) all recorded higher F/SI crashes in the pandemic era.

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Table 10: Crashes by Year and Jurisdiction, All Modes Aggregated (2017-2022) Jurisdiction Year F/SI Other Crashes Total
Atherton 2017-2019 2 116 118
2020-2022 3 41 44
Belmont 2017-2019 5 164 169
2020-2022 5 130 135
Brisbane 2017-2019 8 53 61
2020-2022 8 23 31
Burlingame 2017-2019 3 212 215
2020-2022 11 116 127
Colma 2017-2019 22 9 31
2020-2022 0 1 1
Daly City 2017-2019 4 544 548
2020-2022 39 434 473
East Palo Alto 2017-2019 10 250 260
2020-2022 33 229 262
Foster City 2017-2019 4 106 110
2020-2022 10 74 84
Half Moon Bay 2017-2019 10 77 87
2020-2022 9 82 91
Hillsborough 2017-2019 4 30 34
2020-2022 2 13 15
Menlo Park 2017-2019 23 430 453
2020-2022 15 252 267
Millbrae 2017-2019 11 142 153
2020-2022 24 107 131
Pacifica 2017-2019 21 249 270
2020-2022 24 167 191
Portola Valley 2017-2019 2 25 27
2020-2022 10 11 21
Redwood City 2017-2019 41 783 824
2020-2022 50 546 596
San Bruno 2017-2019 56 266 322
2020-2022 23 187 210
San Carlos 2017-2019 15 155 170

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Jurisdiction Year F/SI Other Crashes Total
2020-2022 19 139 158
San Mateo 2017-2019 58 715 773
2020-2022 23 326 349
South San Francisco 2017-2019 66 530 596
2020-2022 43 424 467
Unincorporated 2017-2019 113 625 738
2020-2022 137 529 666
Woodside 2017-2019 14 73 87
2020-2022 30 87 117
Source: TIMS 2023
Summary by Mode
Table 11 presents countywide crashes broken out by travel mode of the constituent parties. The table shows that pedestrian and bicyclist crashes are disproportionately severe when compared to motor vehicle crashes. Eight percent of motor vehicle injury crashes are F/SI, but the shares are 20 and 17 percent of pedestrian and bicyclist crashes, respectively. These pedestrian and bicyclist shares are higher than their respective 13 percent share of all injury level crashes. These statistics highlight the risk of fatal or severe injuries to people walking or biking when involved in a crash. Among injury crashes alone, pedestrian crashes have approximately double the average EPDO score compared to injury motor vehicle crashes (39.5 compared to 25.6).

Table 11: Crashes by Mode, Countywide (2018-2022)
Mode Count % of 5-year
Total
Crashes
F/SI
Count
% of Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Pedestrians 1,064 13% 208 23% 42,081 19% 39.5
Bicyclists 1,058 13% 175 20% 37,630 17% 35.6
Motor
Vehicle
Alone
6,328 75% 515 57% 136,817 63% 21.6
Total 8,450 100% 898 100% 216,528 100% 25.6
Source: TIMS 2023

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Crashes by Year
Table 12 summarizes crash frequency and severity for all modes by year for the study period. F/SI crash frequency and overall crash frequency and severity (as measured by EPDO) decreased each year from 2019 to 2020 with sharp decreases in the first two pandemic years (2020-21). This reduction is likely related to changes in travel behaviors due to the COVID-19 pandemic safety precautions and the regional stay at home order that was in effect. At the same time, the percrash severity has increased – from 23.2 EPDO per crash in 2018 to 26.5 in 2022.

Table 12: Crashes by Year, All Modes Aggregated (2018-2022) Year Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total F/SI
Crashes
EPDO
Score
% of Total
EPDO
Score
Average EPDO Score
2018 2,068 24% 187 21% 47,949 22% 23.2
2019 1,964 23% 194 22% 48,136 22% 24.5
2020 1,415 17% 166 19% 38,844 18% 27.5
2021 1,428 17% 173 19% 39,679 18% 27.8
2022 1,593 19% 179 20% 42,251 20% 26.5
Total 8,468 100% 899 100% 216,859 100% 25.6
Source: TIMS 2023
Figure 6 displays the annual EPDO scores for each mode, which highlights the overall frequency and severity of crashes. The figure shows:
◼ The annual EPDO scores for motor vehicle crashes decreased from 31,630 in 2018 to 24,621 in 2020, which is the largest net decrease across modes. The annual EPDO values have increased from 2020 but not to pre-pandemic levels.
◼ Motor vehicle and bicyclist EPDO scores have increased by 7 percent and 57 percent, respectively. Pedestrian EPDO scores have fluctuated over time, with no discernably consistent trend.

What this figure does not show is the relative severity on a per-crash basis. The ensuing sections demonstrate the disproportionately high relative severity of pedestrian and bicyclist crashes compared to motor vehicles. The crash trends by mode are described in sections below.

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Figure 6: Annual EPDO Scores by Mode
Source: TIMS 2023
5,508 6,377 5,463 8,440 8,590
8,524 7,138 6,931
7,427 6,150
31,640 31,247
24,621
22,964 26,345
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
2018 2019 2020 2021 2022
Bike EPDO Ped EPDO Motor Vehicle EPDO

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Pedestrians
Table 13 summarizes pedestrian crashes by injury severity and year. Pedestrian crash frequencies were highest during the first two years of the five-year study period and lowest during the core period of the COVID-19 pandemic (2020-2021). Note, however, that pedestrian crashes appear to have slightly increased in severity during the core pandemic years, from an average EPDO score of 39.5 per crash in 2018-2019 to 45.1 per crash in 2020-2021. This finding aligns with national trends in which higher vehicle speeds and higher rates of reckless driving were observed during the pandemic.11 Pedestrian crash frequencies rose again in 2022 as pandemic-related restrictions ease and travel/activities continued to increase, although the overall share of severe and fatal crashes dropped back to about 16 percent (average EPDO score of 33.1).

Table 13: Pedestrian Crashes by Year and Severity (2018-2022) Year Count % of 5-
year
count total
Fatal Severe
Injury
Minor
Injury
Possible
Injury
EPDO % of Total
EPDO
Score
Average
EPDO Score
2018 250 24% 6 44 159 41 9,946 24% 40.6
2019 251 24% 10 35 172 34 9,556 22% 38.5
2020 178 17% 8 33 106 31 7,830 19% 45.1
2021 174 16% 6 33 113 22 7,885 18% 45.1
2022 211 20% 5 28 147 31 6,864 16% 33.1
Total 1,064 100% 35 173 697 159 42,081 100% 40.1
Source: TIMS 2023
11 https://www.ghsa.org/resources/news-releases/GHSA/NHTSA-Speeding-Statement22 https://www.iihs.org/news/detail/pandemic-lockdowns-made-rush-hour-speeding-risky-driving-the-newnormal#:~:text=the%20new%20normal-,Pandemic%20lockdowns%20made%20rush%2Dhour%20speeding%2 C%20risky%20driving%20the%20new,returned%20to%20pre%2Dpandemic%20leve Page 734 of 1188 ls.

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Bicyclists
Table 14 summarizes bicyclist crashes by injury severity and year. Like the other modes, bicyclist crash frequencies were highest during the first two years of the five-year study period and lowest during the core period of the COVID-19 pandemic (2020-2021). As with the other modes, bicyclist crash severity also appeared to increase from pre-pandemic years to pandemic years (average EPDO score of 20 from 2018-2019 compared to 40.2 from 2020-2021). Unlike pedestrian crashes, however, bicyclist crashes appeared to stay more severe in 2022, with an average EPDO score of 43.2.

Table 14: Bicyclist Crashes by Year and Severity (2018-2022) Year Count % of
5-
year
count
total
Fatal Severe
Injury
Minor
Injury
Possible
Injury
EPDO % of
Total
EPDO
Score
Average
EPDO
Score
2018 235 22% 2 25 180 28 6,302 17% 27.3
2019 227 22% 1 32 162 32 7,305 19% 32.7
2020 196 19% 4 25 141 26 6,371 17% 33.3
2021 191 18% 1 43 125 22 8,802 23% 47.0
2022 209 20% 2 40 143 24 8,850 24% 43.2
Total
1,058 100% 10 165 751 132 37,630 100% 36.3
Source: TIMS 2023

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Motor Vehicles
Motor vehicle crashes (see Table 15) reported a large reduction in frequency from 2018, while fatal crashes have fluctuated over time with no discernable trend. There has been a decrease in reported severe injury crashes from 2019 to 2022. From 2018 to 2022, minor injury and possible injury crashes have decreased. As with the other modes, frequency of crashes decreased in 2020 through 2022 as the average EPDO score increased, indicating that fewer, but more severe, crashes were occurring.

Table 15: Motor Vehicle Crashes by Year and Severity
Year Count % of
5-
Year
Total
Fatal Severe Injury Minor
Injury
Possible
Injury
EPDO % of
Total
EPDO
Score
Average
EPDO
Score
2018 1,577 25% 15 95 1,013 454 31,640 23% 20.1
2019 1,483 23% 10 106 900 467 31,247 23% 21.1
2020 1,039 16% 10 86 616 327 24,621 18% 23.7
2021 1,060 17% 15 75 662 308 22,964 17% 21.7
2022 1,169 19% 14 89 724 342 26,345 19% 22.5
Total 6,328 100% 64 451 3,915 1,898 136,817 100% 21.6
Source: TIMS 2023

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Environmental/Land Use
Lighting Conditions
Pedestrians
Table 16 summarizes pedestrian crashes by lighting condition. Most pedestrian crashes (62 percent) occurred during daylight conditions, an expected finding given that most trips are made during daylight conditions. However, lighting condition shows a strong correlation with severity: crashes that occurred in darkness or low-light (i.e., dusk or dawn) conditions were much more likely to result in a F/SI outcome (27 percent) compared to those that occurred during daylight (15 percent). This disproportionality was even stronger in dark, unlit (i.e., lacking streetlight) conditions (32 percent F/SI). While the sample size for dark, unlit conditions was small (n=38), these disparities have been found in national and California-based research on pedestrian fatalities.
12 Lack of visibility and slower detection and reaction times likely contribute to these nighttime crashes. Slower detection and reaction times can result in the motorist traveling at a higher speed (and transferring more kinetic energy) at the time of the crash, leading to a more severe outcome.

Table 16: Lighting Conditions, Pedestrian Crashes (2018-2022) Lighting
Condition
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Daylight 667 62% 100 48% 21,983 52% 33.8
Dark - Street
Lights 325 30% 86 41% 15,944 38% 49.8
Dusk - Dawn 41 4% 10 5% 1,882 5% 47.2
Dark - No Street
Lights 35 3% 11 5% 2,047 5% 60.0
Dark - Street
Lights Not
Functioning 3 <1% 1 1% 213 1% 71.0
Unknown 2 <1% 0 0% 12 <1% 6.0
Total 1,073 100% 208 100% 42,081 100% 40.1
Source: TIMS 2023
12 Sanders, R.L., Schneider, R.J.., & Proulx, F.R. (2022). Pedestrian Fatalities in Darkness: What Do We Know, and What Can Be Done? Transport Policy. 120(May): 23 Page 737 of 1188 -39. DOI: 10.1016/j.tranpol.2022.02.010 February 5, 2024 Page 49
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Bicyclists
Table 17 summarizes bicyclist crashes by lighting condition. The vast majority of bicyclist crashes occurred during daylight conditions (84 percent), which fits with when most bicyclist trips occur.

Like pedestrian crashes, bicyclist crashes were more likely to result in a fatal or severe injury outcome during dark lighting conditions (21 percent) compared to daylight conditions (16 percent). Crash outcomes were even more likely to be severe in dark, unlit conditions (29 percent F/SI), though again the sample size was relatively small (n=14).

Table 17: Lighting Conditions, Bicyclist Crashes (2018-2022) Lighting
Condition
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Daylight 896 84% 137 78% 30,149 80% 34.6
Dark - Street
Lights 108 10% 22 13% 4,375 12% 42.1
Dusk - Dawn 43 4% 11 6% 2,134 6% 50.2
Dark - No Street
Lights 13 1% 4 2% 801 2% 63.6
Unknown 6 1% 1 1% 160 <1% 26.7
Dark - Street
Lights Not
Functioning 1 <1% 0 0% 11 <1% 11.0
Total 1,067 100% 175 100% 37,630 100% 36.3
Source: TIMS 2023

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Motor Vehicles
Table 18 presents motor vehicle crashes by reported lighting conditions. The majority of crashes occurred in daylight conditions (70 percent), which corresponds to when most travel occurs. Of the 6,328 crashes, 22 percent reported to have occurred in the dark where streetlights were present, and 5 percent occurred where streetlights were not present or functioning. Crashes with no streetlights had nearly double the per-crash EPDO score of daylight crashes. Though relatively rare (n=6), crashes that occurred where streetlights were not functioning had an average EPDO score that was more than three times that of crashes that occurred in the dark where streetlights were functioning.

Table 18: Lighting Conditions, Motor Vehicle Crashes (2018-2022) Lighting
Condition
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Daylight 4,447 70% 328 64% 91,247 67% 20.5
Dusk - Dawn 188 3% 12 2% 3,484 3% 18.5
Dark – Street
Lights
1,374 22% 119 23% 30,289 22% 22.0
Dark – No Street
Lights
294 5% 52 10% 10,945 8% 37.2
Dark – Street
Lights Not
Functioning
6 <1% 2 <1% 400 <1% 66.7
Unknown 19 <1% 2 <1% 452 <1% 23.8
Total 6,328 100% 515 100% 136,817 100% 21.6
Source: TIMS 2023

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Weather Conditions
Weather conditions do not appear to be a significant factor in crash patterns in San Mateo County.
◼ More than 90 percent of total crashes and F/SI pedestrian and bicyclist crashes occurred during clear weather conditions. The percentage of F/SI crashes during rainy conditions was slightly higher for pedestrians (six percent) compared to bicyclists (one percent). These findings likely reflect low exposure during rainy conditions, particularly for pedestrians and bicyclists who have access to other transportation options when necessary. ◼ Of motor vehicle crashes, 84 percent occurred during clear weather conditions, 11 percent occurred during cloudy conditions, and 4 percent occurred during rainy conditions.

Roadway Conditions
Pedestrians and Bicyclists
For both modes, the vast majority of crashes occurred at locations with no unusual conditions (98 percent of pedestrian crashes; 95 percent of bicyclist crashes). For bicyclist F/SI crashes (n=175), the majority of F/SI crashes that occurred with any type of non-typical roadway condition (n=17) were solo bicyclist crashes (n=13), underscoring the vulnerability of bicyclists and the need for well-maintained roads. Note that these data may underestimate the degree to which roadway conditions contribute to crashes, particularly for bicyclists, given known patterns of underreporting and common exclusions of solo-bicycle crashes from reported crash data13.

Motor Vehicles
Roadway conditions do not appear to be a significant factor in motor vehicle crash patterns in San Mateo County. Of motor vehicle crashes, 98 percent occurred during no unusual roadway conditions, 1 percent occurred during construction or repair zone conditions, and 1 percent occurred during flooded conditions.

Proximity to Transit Stop
Table 19 summarizes pedestrian crashes that occurred at an intersection within 250 feet of a transit stop. Pedestrian crashes were nearly evenly split between intersections near a transit stop (48 percent of all crashes; 45 percent of F/SI crashes)and those further away. However, crash rates per intersection type indicate a stronger relationship to pedestrian safety than raw crash numbers suggest. Intersections within 250 feet of a transit stop had a crash rate of 20 crashes per 100 intersections, compared to just four crashes per 100 intersections for those intersections further away. This disproportionality may result from higher pedestrian exposure near transit locations and locations in which transit service is provided (employment centers, commercial/retail, higher density housing, etc.), as well as other roadway or crossing 13 https://www.visionzerosf.org/wp-content/uploads/2021/11/Severe Page 740 of 1188 -Injury-Trends 2011-2020 final report.pdf February 5, 2024 Page 52
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characteristics that may increase crash risk (e.g., bus stops being frequently located along arterial or collector streets that are higher-speed, higher-volume streets).

When exploring pedestrian crashes near transit and the presence of intersection signalization, the following patterns were noteworthy:
◼ There is a similar near-even split between signalized intersections (49 percent of all crashes; 53 percent of F/SI crashes) and unsignalized intersections. However, signalized intersections had roughly six times the rate for both crashes per 100 intersections and F/SI crashes per 100 intersections as unsignalized intersections.
◼ F/SI crashes at signalized intersections generally involved the pedestrian crossing and being struck by a motorist going straight (29 percent), or the pedestrian crossing and a motorist turning left (24 percent); both crash types most frequently involved a motorist who failed to yield to the pedestrian (77 percent of all crashes; 50 percent of F/SI crashes). These patterns were similar for unsignalized intersections.
◼ Three-quarters of all crashes and F/SI crashes, regardless of signalization, occurred at an intersection between an arterial (primary, secondary, or tertiary) and a residential street. ◼ Crashes most frequently occurred at a primary-residential intersection (30 percent of all crashes; 28 percent of F/SI crashes), followed by a secondary-residential intersection (28 percent of all crashes; 28 percent of F/SI crashes).

Cumulatively, these findings suggest a need for additional countermeasure support for pedestrians at intersections near transit stops and along arterial roadways. For unsignalized intersections, these countermeasures likely need to focus on driver stopping or yielding, such as through a pedestrian hybrid beacon or roadway reconfiguration and speed reduction measures that change driver thinking, behavior, and detection-reaction time. For signalized intersections, these countermeasures likely need to strengthen separation in time, such as through leading pedestrian intervals and protected left turn signalization. For all intersections, lighting should be considered as an important countermeasure where it does not currently exist.

Table 19: Proximity to Transit Stop, Pedestrian Intersection Crashes (2018-2022) Within 250’ of
transit stops
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO Score
Yes 392 48% 64 45% 13,079 44% 33.7
No 427 52% 78 55% 16,839 56% 39.5
Total 819 100% 142 100% 29,918 100% 36.8
Source: TIMS 2023

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Crash Locations
The following sections discuss crashes by location type. The crashes were assigned as intersection or segment crashes according to the methodology discussed in the Quantifying Crash Severity section on page 23.

Pedestrians
Table 20 summarizes pedestrian crashes by location type. ◼ Most crashes occurred at an intersection (76 percent of all crashes; 68 percent of F/SI crashes), with the remainder coded to segment locations (24 percent of all crashes; 32 percent of F/SI crashes). This distribution is expected, as interactions between pedestrians and motorists typically occur at intersection locations, rather than midblock. ◼ When looking at crash locations and signalization, pedestrian crashes occurred most frequently at unsignalized intersections (48 percent of all crashes; 41 percent of F/SI crashes).
◼ Both segment and unsignalized pedestrian crashes tended to be more severe, with an average EPDO score of 51 and 40.5, respectively, compared to crashes at signalized intersections (average EPDO score of 30.5). Motorist speeds are often higher midblock than at intersections, resulting in higher kinetic energy and limited reaction time, both of which may contribute to greater injury severity from segment crashes.

Countywide sidewalk and driveway data would help us better assess the degree to which pedestrian segment crashes are associated with the absence of sidewalks or presence of driveway locations.

Table 20: Crash Locations, Pedestrian Crashes (2018-2022) Crash Location Count % of 5-
year
Total
Crashes
F/SI
Coun
t
% of
Total
F/SI
Crashe
s
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO Score
Unsignalized
Intersection
515 48% 86 41% 20,717 49% 40.5
Signalized
Intersection
304 28% 56 27% 9,201 22% 30.5
Segment
254 24% 66 32% 12,739 30% 51.0
Total
1,073 100% 208 100% 42,657 100% 40.1
Source: TIMS 2023

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Bicyclists
Table 21 summarizes bicyclist crashes by crash location. Most bicyclist crashes (43 percent) occurred at unsignalized intersections, but most F/SI crashes occurred at segment locations (43 percent). Roughly one-third of the F/SI crashes at unsignalized intersections were solo bicyclist crashes; these crashes were disproportionately severe, which may reflect reporting bias (i.e., people tend not to report minor injury solo bicyclist crashes). Excluding solo bicyclist crashes, most F/SI crashes at unsignalized intersections involved both parties traveling in perpendicular directions, either both going straight (n=7) or the bicyclist going straight and the motorist making a left turn (n=4).

Similar to unsignalized crashes, roughly 40 percent of the segment F/SI crashes involved a solo bicyclist. Excluding solo bicyclist crashes, both parties traveling in the same direction of travel (e.g., an overtaking or dooring crash) was the most common direction of travel for overall crashes (45 percent) and F/SI crashes (58 percent). The most frequent movement combination overall involved both the bicyclist and motorist going straight (14 percent) and among F/SI crashes involved the bicyclist going straight and the motorist making some other unsafe turn (15 percent).

Countywide data on bicycle facilities (including accurate installation dates), number of lanes on the roadway, and presence of on-street parking would allow us to further assess the relationships between specific roadway characteristics and bicycle crashes. For example, with these data, we could gain additional insights about injury severity and crash types relative to bicycle facility type. A complete countywide dataset with detailed bicycle facility data would allow additional insights into bicycle crash risk factors.

Table 21: Crash Locations, Bicyclist Crashes (2018-2022) Crash Location Count % of 5-
year
Total
Crashes
F/SI
Coun
t
% of
Total
F/SI
Crashe
s
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO Score
Unsignalized
Intersection
457 43% 69 39% 17,083 45% 37.7
Segment
386 36% 76 43% 15,685 41% 41.2
Signalized
Intersection
224 21% 30 17% 5,604 15% 25.0
Total
1,067 100% 175 100% 38,372 100% 36.3
Source: TIMS 2023

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Motor Vehicles
Table 22 summarizes frequency and severity for motor vehicle crashes based on location type.

Crashes most frequently occur along roadway segments (39 percent of total, 52 percent of F/SI crashes) occur along roadway segments (i.e., not within an intersection influence area).

Unsignalized intersection crashes are the most severe (average 24.5 EPDO score), followed by segment crashes (average 23.5). Both occupy a larger share of total F/SI crashes and EPDO score compared to their overall share of crashes.

Table 22: Crash Locations, Motor Vehicle Crashes
Crash Location Count % of 5-
year
Total
Crashes
F/SI
Count
% of Total
F/SI
Crashes
EPDO
Score
% of Total
EPDO
Score
Average
EPDO Score
Midblock 2,488 39% 267 52% 64,326 47% 25.9
Unsignalized
Intersection 2,158 34% 159 31% 47,971 35% 22.2
Signalized Intersection 1,678 27% 89 17% 25,702 19% 15.3 Total 6,324 100% 515 100% 137,999 100% 21.8
Source: TIMS 2023

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Intersection Crashes
Roadway Functional Class
The subsequent sections analyze intersection crashes by the functional classification of their constituent roadways according to OSM-provided classifications. For a discussion of those classifications and how they map to the locally defined functional classifications, see the Methodology section beginning on page 24.

Pedestrians
Table 23 summarizes pedestrian intersection crashes by highest and lowest functional classification of the intersecting streets.
◼ The largest share of pedestrian crashes occurred at:
– secondary and residential streets (23 percent of all crashes; 28 percent of F/SI crashes) – primary and residential (20 percent of all crashes; 23 percent of F/SI crashes) – residential and residential (19 percent of all crashes; 13 percent of F/SI crashes) ◼ Intersections between a primary street and either another primary, secondary, or tertiary street had some of the highest rates of F/SI crashes and EPDO scores per 100 intersections.

Compared to streets that intersect with a residential street, there are relatively few intersections between only non-residential streets, but crashes that occur at these locations are disproportionately severe.
◼ Residential-residential intersections accounted for the third largest share of pedestrian crashes but make up 71 percent of the network, resulting in a very low intersection crash rate (1.8 crashes per 100 intersections).

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Table 23: Pedestrian Intersection Crashes by Functional Classification (2018-2022) Higher Order
Roadway
Other
Roadway
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total F/SI
Crashes
EPDO
Score
Crashes
per 100
Ints
F/SI
Crashes
per 100
ints
Trunk total 6 1% 1 1% 170 6.5 1.1
Trunk Trunk 0 0% 0 0% 0 0.0 0.0
Primary 0 0% 0 0% 0 0.0 0.0
Secondary 2 <1% 0 0% 17 25.0 0.0
Tertiary 3 <1% 1 1% 142 30.0 10.0
Residential 1 <1% 0 0% 11 1.4 0.0
Primary total 228 28% 43 30% 7,940 44.3 8.3
Primary Primary 4 1% 3 2% 437 28.6 21.4
Secondary 28 3% 4 3% 728 71.8 10.3
Tertiary 29 4% 4 3% 750 67.4 9.3
Residential 167 20% 32 23% 6,025 39.9 7.6
Secondary total 260 32% 54 38% 11,136 17.3 3.6
Secondary Secondary 19 2% 3 2% 505 19.4 3.1
Tertiary 52 6% 12 9% 2,332 44.8 10.3
Residential 189 23% 39 28% 8,299 14.7 3.0
Tertiary total 167 20% 25 18% 5,710 10.8 1.6
Tertiary Tertiary 38 5% 7 5% 1,288 30.9 5.7
Residential 129 16% 18 13% 4,422 9.0 1.3
Residential total 158 19% 19 13% 4,962 1.8 0.2
Residential Residential 158 19% 19 13% 4,962 1.8 0.2
Source: TIMS 2023

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Bicyclists
Table 24 summarizes bicyclist intersection crashes by highest and lowest functional classification of the intersecting streets.
◼ Like pedestrian crashes, crashes occurred most frequently at:
– secondary-residential intersections (28 percent of all crashes; 24 percent of F/SI crashes) – residential-residential intersections (18 percent of all crashes; 13 percent of F/SI crashes) – primary-residential intersections (17 percent of all crashes; 16 percent of F/SI crashes) ◼ As with pedestrian crashes, In comparison, crashes at intersections with only non-residential streets had a substantially higher rate of crashes and F/SI crashes per 100 intersections than crashes at intersections with residential streets. Intersections involving a residential street accounted for a large share of crashes, but they also represent 96 percent of the network.

Table 24: Bicyclist Intersection Crashes by Functional Classification (2018-2022) Higher
Order
Roadway
Other
Roadway
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
Crashes
per 100
Ints
F/SI
Crashes
per 100
ints
Trunk total 14 2% 4 4% 651 15.1 4.3
Trunk Trunk 3 <1% 0 <1% 33 75.0 0.0
Primary 0 0% 0 <1% 0 0.0 0.0
Secondary 3 <1% 1 1% 137 37.5 12.5
Tertiary 1 <1% 1 1% 120 10.0 10.0
Residential 7 1% 2 2% 361 10.1 2.9
Primary total 169 22% 24 24% 5,155 32.8 4.7
Primary Primary 5 1% 2 2% 339 35.7 14.3
Secondary 27 4% 2 2% 510 69.2 5.1
Tertiary 24 4% 4 4% 680 55.8 9.3
Residential 113 17% 16 16% 3,626 27.0 3.8
Secondary
total 259 38% 37 37% 8,464 17.3 2.5
Secondary Secondary 40 6% 6 6% 1,343 40.8 6.1
Tertiary 31 5% 7 7% 1,307 26.7 6.0
Residential 188 28% 24 24% 5,814 14.6 1.9
Tertiary total 120 18% 21 21% 48,87 7.7 1.4
Tertiary Tertiary 12 2% 3 3% 672 9.8 2.4
Residential 108 16% 18 18% 4,215 7.6 1.3
Residential
total 119 18% 13 13% 3,558 1.4 0.1
Residential Residential 119 18% 13 13% 3,558 1.4 0.1
Source: TIMS 2023 Page 747 of 1188
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Motor Vehicles
Table 25 presents motor vehicle crashes by functional classification. The following disproportionate relationships stand out:
◼ Intersection crashes along primary and secondary roads account for the majority of intersection crashes (36 and 29 percent each) and similarly large shares of F/SI crashes (35 and 29 percent).
◼ The highest average EPDO scores were at intersections along the trunk and primary roadway system where they intersect with lower-order intersections. When looking at the signalized versus unsignalized breakdown of these combinations, the unsignalized intersections all have higher average EPDO scores—indicating that unsignalized minor-street intersections along arterial roadways have a disproportionate severity burden in the county. ◼ Trunk and residential unsignalized intersections consist of two percent of total crashes but make up for six percent of total F/SI crashes.

Table 25: Motor Vehicle Intersection Crashes by Functional Classification Higher
Order
Roadway
Other
Roadway
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
Crashes
per 100
Ints
F/SI
Crashes
per 100
ints
Trunk total 164 4% 17 7% 4,442 176.3 176.3
Trunk Trunk 31 1% 0 0% 286 775.0 775.0
Primary - 0% 0 0% - 0.0 0.0
Secondary 31 1% 4 2% 900 387.5 387.5
Tertiary 15 0% 1 0% 320 150.0 150.0
Residential 87 2% 12 5% 2,936 126.1 126.1
Primary total 1,387 36% 86 35% 24,738 269.3 269.3
Primary Primary 50 1% 6 2% 1,398 357.1 357.1
Secondary 209 5% 12 5% 3,332 535.9 535.9
Tertiary 196 5% 11 4% 3,201 455.8 455.8
Residential 932 24% 57 23% 16,807 222.4 222.4
Secondary
total
1,121 29% 72 29% 21,631 74.7 74.7
Secondary Secondary 139 4% 12 5% 3,027 141.8 141.8
Tertiary 174 5% 15 6% 3,837 150.0 150.0
Residential 808 21% 45 18% 14,767 62.8 62.8
Tertiary total 546 14% 39 16% 11,725 35.2 35.2
Tertiary Tertiary 129 3% 10 4% 2,490 104.9 104.9
Residential 417 11% 29 12% 9,235 29.2 29.2
Residential
total
618 16% 34 14% 11,137 7.0 7.0
Residential Residential 618 16% 34 14% 11,137 7.0 7.0

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Intersection Control
Pedestrians
Table 26 summarizes pedestrian intersection crashes by intersection signalization.

Most intersection crashes (63%) and F/SI crashes (61%) occurred at unsignalized intersections. This finding is expected as the vast majority of intersections across the county are unsignalized intersections, and these intersections include uncontrolled locations along higher-traffic streets.

However, when looking at crashes per 100 intersections, crashes were concentrated at signalized intersections (50.9 crashes per 100 intersections; 9.4 F/SI crashes per 100 intersections).

At signalized intersections, the following pre-crash movement trends were most prevalent:
◼ a pedestrian crossing in a crosswalk who was struck by a left turning motorist (38 percent of all crashes; 29 percent of F/SI crashes), followed by
◼ a pedestrian crossing in a crosswalk who was struck by a right turning motorist (23 percent of all crashes; 9 percent of F/SI crashes).

Looking just at injury severity per driver pre-crash movement at signalized intersections, the following trends stand out:
◼ 40 percent of crashes involving motorists proceeding straight resulted in a F/SI outcome, while
◼ 15 percent of crashes involving a left-turning driver resulted in an F/SI outcome.

Crashes that involved a pedestrian crossing in a crosswalk at a signalized intersection with a motorist going straight accounted for the second largest share of F/SI crashes (23 percent); these crashes were also disproportionately severe when they occurred, with 42 percent of crashes resulting in a F/SI outcome.

At unsignalized intersections, the following pre-crash movement trends were most prevalent:
◼ a pedestrian crossing in a crosswalk who was struck by motorist going straight (24 percent of all crashes; 27 percent of F/SI crashes), and
◼ a pedestrian crossing in a crosswalk who was struck by motorist making a left turn (22 percent of all crashes; 22 percent of F/SI crashes).

Looking just at injury severity per driver pre-crash movement at unsignalized intersections, the following trends stand out:
◼ 22 percent of crashes involving a motorist going straight resulted in a F/SI outcome, ◼ 16 percent of crashes involving a motorist turning left resulted in a F/SI outcome, and ◼ 9 percent of crashes involving a right turning motorist resulted in an F/SI outcome.

This order of F/SI outcomes by motorist pre-crash movement likely directly reflects the motorist’s speed at the time of the crash. Proceeding straight and turning left are generally associated with higher vehicle speeds compared to turning right.

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Table 26: Intersection Signalization, Pedestrian Intersection Crashes (2018-2022) Traffic Signal Count % of
5-year
Total
Crash
es
F/SI
Count
% of
Total F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Avg
EPDO
Score
Crashes
per 100
Ints
F/SI
Crashes
per 100
Ints
Signalized 304 37% 56 39% 9,201 31% 30.5 50.9 9.4
Unsignalized 515 63% 86 61% 20,717 69% 40.5 4.3 0.7
Total 819 100% 142 100% 29,918 100% 36.8 6.6 1.1
Source: TIMS 2023
Bicyclists
Table 27 summarizes bicyclist intersection crashes by intersection signalization. Sixty-seven percent of all crashes and 70 percent of F/SI crashes occurred at unsignalized intersections.

When looking at crashes per 100 intersections, crashes were concentrated at signalized intersections (37.5 crashes per 100 intersections; 5 F/SI crashes per 100 intersections). The percentage of crashes that resulted in a F/SI outcome was comparable between location types, with 15 percent of crashes resulting in a F/SI outcome at unsignalized intersections compared to 13 percent at signalized intersections.

At signalized intersections, the following pre-crash movements between a motorist and bicyclist were the most prevalent:
◼ Perpendicular pre-crash directions of travel (48 percent of all crashes; 50 percent of F/SI crashes) and
◼ Same direction movements (32 percent of all crashes; 31 percent of F/SI crashes).

Perpendicular F/SI crashes most commonly involved a bicyclist proceeding straight and the motorist proceeding straight (31 percent), followed by a bicyclist proceeding straight and the motorist making a left turn (12 percent).

In terms of violation type at signalized intersections:
◼ Disregarding a red traffic signal was the most frequent reported violation type for all crashes (22 percent) and F/SI crashes (39 percent), followed by ◼ Improper turning (15 percent of all crashes; 12 percent of F/SI crashes).

The same trends in pre-crash movements existed at unsignalized intersections, with some differences in severity. The following pre-crash movements between a motorist and bicyclist were the most prevalent:
◼ Perpendicular pre-crash directions of travel (49 percent of all crashes; 45 percent of F/SI crashes) and Page 750 of 1188
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◼ Same direction (28 percent of all crashes; 38 percent of F/SI crashes).

Most perpendicular crashes occurred at intersections between residential-residential streets (30 percent) and secondary-residential streets (29 percent), though crashes were more likely to result in a F/SI outcome or a higher EPDO at locations between any arterial type (primary, secondary, and tertiary) and a residential street, compared to an intersection only between residential streets.

This finding may reflect both the higher speeds of arterial streets and the challenges a bicyclist may experience when trying to cross a larger street without a traffic control device to accommodate a safe and comfortable crossing.

Table 27: Intersection Signalization, Bicyclist Intersection Crashes (2018-2022) Traffic
Signal
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Crashes
per 100
Ints
F/SI
Crashes
per 100
Ints
Signalized 224 33% 30 30% 5,604 25% 25.0 37.5 5.0
Unsignalized 457 67% 69 70% 17,083 75% 37.7 3.9 0.6
Total 681 100% 99 100% 22,687 100% 33.5 5.5 0.8
Source: TIMS 2023

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Motor Vehicles
Table 28 presents motor vehicle crashes by intersection control. The majority of motor vehicle crashes occurred at unsignalized intersections, but signalized intersections have a much higher crash rate per 100 intersections. This reflects the roadway network: unsignalized intersections are far more abundant (95 percent of countywide intersections), but signalized intersections carry higher traffic volumes.

In terms of severity, the average EPDO score at unsignalized intersections is higher as previously noted, and the EPDO score share is higher than the crash share.

Table 28: Motor Vehicle Intersection Crashes Intersection Control Traffic
Signal
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Crashes
per 100
Ints
F/SI
Crashes
per 100
Ints
Signalized 1,678 44% 89 36% 25,702 35% 15.3 281.1 14.9
Unsignalized 2,158 56% 159 64% 47,971 65% 22.2 18.2 1.3 Total 3,836 100% 248 100% 73,673 100% 19.2 30.8 2.0
Source: TIMS 2023

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Number of Legs
Pedestrians
Table 29 summarizes pedestrian crashes at intersections by the number of intersection legs.

Interestingly, three-quarters of the network is comprised of three-legged intersections. However, most pedestrian crashes (59 percent) and F/SI crashes (61 percent) occurred at four-legged intersections. While four-legged intersections only represent about a quarter of intersections, these locations are often riskier than three-legged intersections due to pedestrians having higher exposure levels to moving vehicles, resulting in a higher frequency of interactions that can lead to crashes. The average EPDO scores are essentially the same between three- and four-legged intersections, though crashes are much more concentrated at four-legged intersections when looking at crashes and F/SI crashes per 100 intersections. When looking at signalization and number of legs, crashes most frequently occurred at unsignalized four-legged intersections (33 percent of all crashes; 35 percent of F/SI crashes), although crashes and F/SI crashes are concentrated at signalized four-legged intersections, which have roughly six times the rate of crashes and F/SI crashes per 100 intersections.

Table 29: Number of Legs, Pedestrian Intersection Crashes (2018-2022) # of
Legs
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total F/SI
Crashes
EPDO
Score
% of Total
EPDO
Score
Average
EPDO
Score
Crashes
per 100
Ints
F/SI
Crashes
per 100
Ints
3 319 389% 53 37%
11,54
3 39% 36.2 3.4 0.6
4 485 59% 86 61%
17,89
8 60% 36.9 16.3 2.9
5+ 15 2% 3 2% 477 2% 31.8 28.8 5.8
Total
819 100% 142 100%
29,91
8 100% 36.5 6.6 1.1
Source: TIMS 2023

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Bicyclists
Table 30 summarizes bicyclist crashes at intersections by number of legs. Unlike pedestrian crashes, most bicyclist crashes (52 percent) and F/SI crashes (55 percent) occurred at threelegged intersections. However, the three-legged locations still had a lower rate of crashes and F/SI crashes per 100 intersections compared to four-legged intersections. As with pedestrians, the higher rate of bicyclist crashes at four-legged intersections likely relates to the role these intersections play in moving traffic throughout the county, and the resultant greater exposure of bicyclists to motor vehicles at these locations. For both pedestrians and bicyclists, greater exposure increases crash risk unless effective safety countermeasures are in place. When looking at signalization, intersections with traffic signals had a higher concentration of crashes (crashes per 100 intersections) for each number of legs category and tended to be more severe with having a higher average EPDO score compared to unsignalized intersections.

Table 30: Number of Legs, Bicyclist Intersection Crashes (2018-2022) # of
Legs
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total F/SI
Crashes
EPDO
Score
% of Total
EPDO
Score
Average
EPDO
Score
Crashes
per 100
Ints
F/SI
Crashes
per 100
Ints
3 355 52% 54 55%
12,59
5 55% 35.5 3.8 0.6
4 322 47% 45 46%
10,07
6 44% 31.3 10.8 1.5
5+ 4 1% 0 0% 44 <1% 11.0 7.7 0.0
Total 681 100% 99 100% 22,71
5 100% 33.5 5.5 0.8
Source: TIMS 2023

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Speed Limit
The following sections present intersections crashes by maximum posted speed limit and compare the results to the total mileage of roadway by speed limit. First, The summary of roadways by speed limit is provided in Table 31 for context. Note that roadways with posted speeds 45 mph or higher account for 11 percent of the total roadway network, whereas roadways with posted speed 25 mph or less account for 80 percent. The findings below show a much higher share of crashes occurring on those higher speed roadways.

Table 31: County Roadway Mileage by Posted Speed Limit
Posted
Speed
Limit
Length
(miles)
Share of
Total
Roadway
Mileage
≤25 2306 80%
30 34 1%
35 193 7%
40 17 1%
45 234 8%
50+ 92 3%
Total 2877 100%
Source: C/CAG 2023

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Pedestrians
Table 32 summarizes pedestrian crashes by the posted speed limit. We found that 25-mph streets accounted for the largest share of pedestrian crashes (38 percent) and F/SI crashes (34 percent). This distribution makes sense, given that 80 percent of the street network has a speed limit of 25mph or less. As such, the rate of crashes and average EPDO scores per 100 miles is the lowest for 25 mph of all posted speed limits. Streets with a posted speed limit of 45 mph had the second largest share of crashes (27 percent) and F/SI crashes (29 percent), followed by 35-mph streets (23 percent of all crashes; 27 percent of F/SI crashes). These higher speed streets had some of the highest percentages of crashes resulting in an F/SI outcome, average EPDO scores, and EPDO scores per 100 miles. These findings support the current understanding that higher speed roads have serious safety implications for vulnerable road users.

Table 32: Posted Speed Limit, Pedestrian Crashes (2018-2022) Posted
Speed
Limit
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
EPDO/
Crash
EPDO/
100
Miles
25 400 38% 69 34% 15,366 37% 38.4 666
30 82 8% 11 5% 2,586 6% 31.5 7,511
35 237 23% 55 27% 10,299 25% 43.5 5,339
40 34 3% 4 2% 972 2% 28.6 5,570
45 277 27% 59 29% 11,626 28% 42.0 4,971
50+ 13 1% 5 3% 728 2% 56.0 790
Total 1,043 100% 203 100% 41,577 100% 39.9 1,445
Source: TIMS 2023

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Bicyclists
Table 33 summarizes bicyclist crashes by the posted speed limit. Streets with a posted speed limit of 25 mph (32 percent of all crashes; 31 percent of F/SI crashes) accounted for the second largest share of crashes but given that these streets represent 80 percent of the network, they had the lowest rate of EPDO scores (n=202) on a 100-mile basis. In contrast, higher-speed streets tend to have very high EPDO scores per 100 miles.

The following streets accounted for the largest share of F/SI crashes and the highest rates of EPDO per 100 miles:
◼ 45 mph accounted for the largest share of all crashes (32 percent) and F/SI crashes (31 percent) and had the highest rate of EPDO scores per 100 miles (2,129), while 45-mph streets comprise only eight percent of the network.
– Additional exploration of streets with a posted speed limit of 45 mph shows that the largest share of crashes (37 percent) and F/SI crashes (45 percent) occurred at segment (non-intersection) locations.
– Most segment crashes and F/SI crashes involved a bicyclist and motorist traveling in the same direction. These same direction crashes often involved violations pertaining to unsafe speed and improper turns.
◼ 35 mph accounted for the third largest share of all crashes (25 percent) and F/SI crashes (27 percent) but had the second highest rate of EPDO scores per 100 miles (n=1,713), while 35- mph streets comprise only seven percent of the network. – Crashes along 35-mph streets most frequently occurred at unsignalized intersections (40 percent of all crashes; 47 percent of F/SI crashes).
– These crashes at unsignalized intersections generally involved both roadway users traveling in perpendicular directions (39 percent of all crashes; 27 percent of F/SI crashes). These perpendicular direction crashes often involved violations pertaining to failing to stop at a stop sign.

Table 33: Posted Speed Limit, Bicycle Crashes (2018-2022) Posted
Speed
Limit
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
EPDO/
Crash
EPDO/
100 Mile
25 330 32% 231 31% 4,648 32% 14.1 202
30 51 5% 44 6% 73 1% 1.4 212
35 261 25% 200 27% 3,304 23% 12.7 1,713
40 29 3% 24 3% 70 1% 2.4 401
45 330 32% 233 31% 4,980 34% 15.1 2,129
50+ 31 3% 19 3% 1,444 10% 46.6 1,566
Total 1,032 100% 751 100% 14,519 100% 14.1 505

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Motor Vehicles
Table 34 presents motor vehicle crashes by intersection control and posted speed limit. Crashes most frequently occurred at intersections with a posted speed limit of 40 mph or higher (41 percent and 45 percent of F/SI crashes), although those roadways account for just 12 percent of the roadway network. Roadways posted at 40-45 mph show the highest EPDO per 100 miles, as well.

The higher-speed intersection primarily occurred at unsignalized intersections:
◼ 70 percent of 40mph intersections
◼ 55 percent of 45 mph intersections
◼ 61 percent of 50+ mph intersections
Notably, almost 47 percent of the 40mph+ unsignalized intersection crashes occurred along secondary roadways A majority of crashes at 45 mph or higher intersections were at unsignalized intersections (56 percent), and most of the frequency and severity was along secondary roadways intersecting lower-order roadways (48 percent of EPDO).

Table 34: Motor Vehicle Intersection Crashes by Posted Speed Limit Posted
Speed
Limit
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
EPDO/
Crash
EPDO/
100 Mile
25 1,081 28% 66 27% 20,785 28% 19.2 901
30 218 6% 10 4% 3,439 5% 15.8 9,988
35 931 24% 60 24% 17,072 23% 18.3 8,850
40 166 4% 10 4% 3,288 4% 19.8 18,842
45 1276 33% 88 35% 25,384 34% 19.9 10,853
50+ 164 4% 14 6% 3,705 5% 22.6 4,019
Total 3,836 100% 248 100% 73,673 100% 19.2 2,561
Source: TIMS 2023

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Roadway Crash Geometric Characteristics
Functional Classification
Table 35 presents the roadway functional classification and posted speed limit countywide distribution. The majority (84 percent) of the countywide roadway network has a posted speed limit of 25 mph or less and the majority (66 percent) of roadways are classified as residential.

Table 35: Roadway Functional Class and Posted Speed Distribution, Countywide Functional
Classification
Posted Speed
Total
<=25 30-35 40+
Trunk 0% 0% 1% 1%
Primary <1% 1% 1% 3%
Secondary 2% 2% 3% 7%
Tertiary 5% 2% 1% 8%
Residential 64% 2% <1% 66%
Other <1% 0% <1% 1%
Unknown 13% <1% 1% 15%
Total 84% 8% 8% 100%
Source: C/CAG, OSM 2023

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Pedestrians
Table 36 summarizes pedestrian crashes by functional classification. Most pedestrian crashes (29 percent) and F/SI crashes (31 percent) occurred along secondary streets. The distribution of pedestrian crashes is somewhat uniform between secondary, primary, residential, and tertiary streets, with the percentage of crashes by functional class ranging between 20 percent (tertiary) and 29 percent (secondary). However, when looking at mileage-based crash rates, primary streets had the highest rate of crashes per 100 miles (340 crashes per 100 miles) and F/SI crashes (81 F/SI crashes per 100 miles), followed by secondary (137 crashes per 100 miles; 29 F/SI crashes per 100 miles). This supports the current understanding of pedestrian crash risk along major arterials, which generally have higher vehicle speeds, higher vehicle volumes, and greater widths to cross.

Table 36: Functional Classification, Pedestrian Crashes (2018-2022) Functional
Class
Miles Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Crashes
per 100
miles
F/SI
Crashes
per 100
miles
Secondary 225 308 29% 65 31% 13,307 31% 137 29
Primary 78 264 25% 63 30% 11,385 27% 13 2
Residential
1,82
4 242 23% 32 15% 7,806 18% 89 17
Tertiary 246 219 20% 41 20% 8,711 20% 340 81
Unknown 30 3% 5 2% 1,080 3% 20 4
Trunk 49 10 1% 2 1% 368 1% 137 29
Total 1,073 100% 208 100% 42,657 100% 36 7
Source: TIMS 2023

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Bicyclists
Table 37 summarizes bicyclist crashes by functional classification. Most bicyclist crashes (39 percent) occurred along secondary streets, followed by residential (20 percent) and primary (20 percent) streets. Residential streets accounted for the second largest share of crashes and third largest share of F/SI crashes but had the lowest rate of crashes per 100 miles (11) and F/SI crashes per 100 miles (7) compared to primary streets (260 crashes per 100 miles; 224 F/SI crashes per 100 miles) and secondary streets (177 crashes per 100 miles; 128 KS crashes per 100 miles).

Like pedestrian crashes, these findings likely indicate the vulnerability of bicyclists traveling along or across major streets that generally have higher vehicle volumes and speeds and are larger streets to cross.

When looking at crashes along secondary streets, most crashes occurred at unsignalized intersections (43 percent), and most F/SI crashes occurred at segment locations (44 percent).

Most crashes at unsignalized locations involved both roadway users traveling in perpendicular directions of travel (41 percent), while most segment F/SI crashes involved a solo bicyclist (45 percent) or the motorist and bicyclist traveling in the same direction (38 percent).

Table 37: Functional Classification, Bicycle Crashes (2018-2022) Functional
Class
Miles Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Crashes
per 100
miles
F/SI
Crashes
per 100
miles
Secondary 225 399 39% 288 38.% 5,930 41% 177 128
Residential 1,824 236 20% 135 18% 3,347 23% 11 7
Tertiary 246 203 18% 131 17% 2,401 17% 77 53
Primary 78 151 20% 174 23% 1,142 8% 260 224
Trunk 49 35 3% 20 3% 1,104 8% 59 41
Ramp 88 30 1% 1 <1% 237 2% 9 1
Alley 21 8 <1% 2 <1% 341 2% 15 10
Motorway 3 3 <1% 0 0% 17 <1% 61 <1%
Total 1,032 100% 751 100% 14,519 100% 36 26
Source: TIMS 2023

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Motor Vehicles
Table 38 presents motor vehicle roadway crashes by functional classification. Secondary roadways are overrepresented in in F/SI share (36 percent) compared to total share (29 percent). Both of those shares are higher than the total secondary roadway share—just 8 percent of the county roadway network. Tertiary roadways are similarly overrepresented – 23 percent of F/SI and 29 percent of total compared to 9 percent of the roadway network. The tertiary crashes have by far the largest crashes and F/SI crashes per 100 miles of roadway network.

Table 38: Functional Classification, Motor Vehicle Crashes (2018-2022) Functional
Class
Miles Count % of 5-
year
Total
Crashes
F/SI
Cou
nt
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Crashes
per 100
miles
F/SI
Crashes
per 100
miles
Secondary 225 1,828 29% 184 36% 45,476 33% 813.8 81.9
Residential 1,824 1,065 17% 64 12% 19,968 14% 58.4 3.5
Tertiary 246 1,819 29% 121 23% 34,058 25% 2,342.3 155.8 Primary 78 843 13% 69 13% 19,179 14% 343.1 28.1
Trunk 49 409 6% 58 11% 13,056 9% 828.9 117.6
Ramp 88 187 3% 6 1% 2,687 2% - -
Alley 21 165 3% 10 2% 3,035 2% 187.6 11.4
Motorway 3 6 0% 3 1% 523 0% 1.7 0.9
Total 1 0% 0 0% 6 0% 4.9 0.0
Source: TIMS 2023

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Speed Limit
Pedestrian
Table 39 summarizes pedestrian crashes by the posted speed limit. We found that 25-mph streets accounted for the largest share of pedestrian crashes (38 percent) and F/SI crashes (34 percent). This distribution makes sense, given that 80 percent of the street network has a speed limit of 25 mph or less. As such, the rate of crashes and average EPDO scores per 100 miles is the lowest for 25 mph of all posted speed limits. Streets with a posted speed limit of 45 mph had the second largest share of crashes (27 percent) and F/SI crashes (29 percent), followed by 35-mph streets (23 percent of all crashes; 27 percent of F/SI crashes). These higher speed streets had some of the highest percentages of crashes resulting in an F/SI outcome, average EPDO scores, and EPDO scores per 100 miles. These findings support the current understanding that higher speed roads have serious safety implications for vulnerable road users.

Table 39: Posted Speed Limit, Pedestrian Crashes (2018-2022) Posted
Speed
Limit
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
EPDO/
Crash
EPDO/
100
Miles
25 400 38% 69 34% 15,366 37% 38.4 666
30 82 8% 11 5% 2,586 6% 31.5 7,511
35 237 23% 55 27% 10,299 25% 43.5 5,339
40 34 3% 4 2% 972 2% 28.6 5,570
45 277 27% 59 29% 11,626 28% 42.0 4,971
50 13 1% 5 3% 728 2% 56.0 790
Total 1,043 100% 203 100% 41,577 100% 39.9 1,445
Source: TIMS 2023

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Bicyclist
Table 40 summarizes bicyclist crashes by the posted speed limit. Streets with a posted speed limit of 25 mph (32 percent of all crashes; 31 percent of F/SI crashes) accounted for the second largest share of crashes, but given that these streets represent 80 percent of the network, they had the lowest rate of EPDO scores (n=202) on a 100-mile basis. In contrast, higher-speed streets tend to have very high EPDO scores per 100 miles.

The following streets accounted for the largest share of F/SI crashes and the highest rates of EPDO per 100 miles:
◼ 45 mph accounted for the largest share of all crashes (32 percent) and F/SI crashes (31 percent) and had the highest rate of EPDO scores per 100 miles (n=2,129), while 45-mph streets comprise only eight percent of the network.
– Additional exploration of streets with a posted speed limit of 45 mph shows that the largest share of crashes (37 percent) and F/SI crashes (45 percent) occurred at segment (non-intersection) locations.
– Most segment crashes and F/SI crashes involved a bicyclist and motorist traveling in the same direction. These same direction crashes often involved violations pertaining to unsafe speed and improper turns.
◼ 35 mph accounted for the third largest share of all crashes (25 percent) and F/SI crashes (27 percent) but had the second highest rate of EPDO scores per 100 miles (n=1,713), while 35- mph streets comprise only seven percent of the network. – Crashes along 35-mph streets most frequently occurred at unsignalized intersections (40 percent of all crashes; 47 percent of F/SI crashes).
– These crashes at unsignalized intersections generally involved both roadway users traveling in perpendicular directions (39 percent of all crashes; 27 percent of F/SI crashes). These perpendicular direction crashes often involved violations pertaining to failing to stop at a stop sign.

Table 40: Posted Speed Limit, Bicycle Crashes (2018-2022) Posted
Speed
Limit
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
EPDO/
Crash
EPDO/
100
Miles
25 330 32% 231 31% 4,648 32% 14.1 202
30 51 5% 44 6% 73 1% 1.4 212
35 261 25% 200 27% 3,304 23% 12.7 1,713
40 29 3% 24 3% 70 1% 2.4 401
45 330 32% 233 31% 4,980 34% 15.1 2,129
50+ 31 3% 19 3% 1,444 10% 46.6 1,566
Total 1,032 100% 751 100% 14,519 100% 14.1 505

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Motor Vehicles
Table 41 presents motor vehicle crashes by posted max speed limit. Roadways with posted speed limits of 40 mph or higher consist of 12 percent of County roadways but make up 44 percent total crashes and the majority (55 percent) of total F/SI crashes. Crashes that occurred on roadways with a posted speed limit of 40 mph or higher had an average EPDO per mile that was more than thirteen times that of crashes occurring on roadways with a posted speed limit of 25 mph or less.

The majority of the roadways (80 percent) in the County have a posted speed limit of 25 mph or less and make up 28 percent of total crashes and 23 percent of total F/SI crashes.

Speed plays a role in crash risk and severity in the following three ways:
◼ Higher vehicle speeds involve more kinetic energy transfer, resulting in more severe outcomes.
◼ Drivers traveling at higher speeds have a narrower field of vision. ◼ Drivers have less margin for reaction times and require greater distance both to react and to brake or avoid a conflict, making crashes more likely.

Table 41: Speed Limit, Motor Vehicle Crashes
Posted
Speed
Limit
Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
EPDO/
Crash
EPDO/
Miles
<=25 1,760 28% 118 23% 35,196 26% 20.0 1,526
30 299 5% 16 3% 5,074 4% 17.0 14,737
35 1310 21% 92 18% 25,611 19% 19.6 13,277
40 222 4% 13 3% 4,311 3% 19.4 24,705
45 2,092 33% 209 41% 51,557 37% 24.6 22,044
50+ 454 7% 61 12% 13,563 10% 29.9 14,712
Unknown 187 3% 6 1% 2,687 2% 14.4 -
Total 6,324 100% 515 100% 137,999 100% 21.8 4,796
Source: TIMS 2023

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Crash Dynamics/Behaviors
Crash Type/Pre-Crash Movement Analysis
This section analyzes pre-crash movements and crash types. The crash type coding available in the data are more meaningful for describing vehicle-to vehicle or solo vehicle dynamics than for pedestrian and bicyclists crashes. Therefore, for pedestrian and bicyclist crashes, pre-crash movement is used here as a more useful way to determine crash dynamics while the motor vehicle analysis uses crash types.

Pedestrians
Table 42 summarizes the top ten pedestrian crashes by pre-crash movements. Most crashes involved:
◼ A pedestrian crossing at an intersection with a motorist making a left turn (24 percent) ◼ A pedestrian crossing at an intersection with a motorist proceeding straight (15 percent) ◼ A pedestrian crossing not in a crosswalk with a motorist proceeding straight (13 percent) Of those three crash types, those that involved a motorist proceeding straight had a higher share of crashes that resulted in a F/SI outcome and a higher average EPDO score. These findings likely relate to higher vehicle speeds at the time of the crash, which lead to a more severe pedestrian injury.

Most crashes involving a motorist making a left turn and the pedestrian crossing in a crosswalk occurred at a signalized intersection (45 percent), but most of the F/SI crashes with this crash type occurred at unsignalized intersections. These F/SI crash types occurred most frequently at secondary streets; along these streets, crashes were generally split between signalized and unsignalized intersections. The vast majority of this crash type was associated with the motorist failing to yield to the pedestrian (87 percent of all crashes; 85 percent of F/SI crashes of this type). Additionally, most of crashes that involved a pedestrian crossing in a crosswalk and a motorist making a left turn occurred during daylight conditions (73 percent of all crashes; 83 percent of F/SI crashes) and were on average more severe than crashes that occurred during dark lighting conditions.

Crashes that involved a pedestrian crossing an intersection with a motorist proceeding straight most frequently occurred at unsignalized intersections for overall crashes (77 percent) and F/SI crashes (59 percent). This may be a result of the pedestrian attempting to cross a larger street who was struck by a motorist who did not have a stop sign or signal. Furthermore, crashes at these unsignalized locations most often involved a motorist who did not yield to the crossing pedestrian (80 percent of all crashes; 87 percent of F/SI crashes). Additionally, most F/SI crashes for this crash type (62 percent) occurred during dark lighting conditions, which is higher than the percentage in darkness for all pedestrian crashes countywide (47 percent).

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Table 42: Movement-Based Crash Types, Pedestrian Crashes (2018-2022) Movement-Based Crash
Types, Pedestrian
Count % of
Crashes
# of F/SI
Crashes
% of
F/SI
Crashe
s
EPDO
Score
% of
Total
EPDO
Score
Avg
EPDO
Score
Ped Crossing in Crosswalk at
Intersection, MV Making Left
Turn
256 24% 40 19% 8,570 20% 33.5
Ped Crossing in Crosswalk at
Intersection, MV Proceeding
Straight
163 15% 39 19% 7,696 18% 47.5
Ped Crossing Not in
Crosswalk, MV Proceeding
Straight
139 13% 49 24% 9,005 21% 64.8
Ped Crossing in Crosswalk at
Intersection, MV Making
Right Turn
122 11% 11 5% 2,826 7% 23.2
Ped In Road, Including
Shoulder, MV Proceeding
Straight
80 8% 19 9% 3,697 9% 46.8
Ped Crossing in Crosswalk at
Intersection, MV Not Stated
26 2% 3 1% 654 2% 25.2
Ped Crossing Not in
Crosswalk, MV Making Left
Turn
24 2% 6 3% 1,151 3% 48.0
Ped Crossing in Crosswalk
Not at Intersection, MV
Proceeding Straight
17 2% 5 2% 1,014 2% 63.4
Ped Not in Road, MV
Backing
16 2% 2 1% 484 1% 30.3
Ped Not in Road, MV
Proceeding Straight
13 1% 4 2% 724 2% 60.3
Other
217 20% 30 15% 6,836 16% 31.5
Total 1,073 100% 208 100% 42,657 100% 40.1
Source: TIMS 2023 Page 767 of 1188
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Crashes that involved a pedestrian crossing not in a crosswalk with a motorist proceeding straight occurred most often at segment locations (46 percent of all crashes; 53 percent of F/SI crashes), followed by unsignalized intersections (39 percent of all crashes; 31 percent of F/SI crashes). Some caution should be used when interpreting the reported action of the pedestrian crossing within a crosswalk or not, as there may be some reporting errors related to a pedestrian crossing within an unmarked crosswalk being coded as not in crosswalk. Regardless of location type (signalized, unsignalized, or segment), most crashes involved the motorist not yielding to the pedestrian (71 percent of all crashes; 59 percent of F/SI crashes).

Crashes that occurred at segment locations tend to be more severe, having a higher percentage of crashes that resulted in a F/SI outcome (41 percent) and a higher average EPDO score (73) compared to intersection crashes (31 percent of crashes resulting in a F/SI outcome; 58 average EPDO). This difference in injury severity outcomes likely reflects higher vehicle speeds at the time of the crash. Additionally, most F/SI crashes with this crash type (71 percent) occurred during dark lighting conditions – much higher than the percentage of pedestrian crashes for all crash types countywide (47 percent). This combination of midblock crossing, higher vehicle speeds, and dark lighting conditions may be a key factor in severe pedestrian crashes throughout the county.

Bicyclists
Table 43 summarizes the top 10 movement-based bicyclist crash types for crashes that involved at least one bicyclist and one motorist, solo bicyclist crashes are excluded. Most crashes involved both roadway users proceeding straight at the time of the crash for both the overall crashes (21 percent) and F/SI crashes (18 percent). This finding is not surprising, given that velocity for roadway users going straight is generally higher than for other pre-crash movements, and higher velocities reduce reaction time and increase injury severity in the event of a crash.

These crashes occurred most frequently at unsignalized intersections (53 percent), whereas F/SI crashes occurred most frequently at signalized intersections (43 percent). The most frequent violation types relate to failure to stop at a stop sign (15 percent of call crashes; 19 percent of F/SI crashes), disregarding a traffic signal (12 percent of all crashes; 29 percent of F/SI crashes), and unsafe speed (11 percent of all crashes; 5 percent of F/SI crashes).

The second most common crash type involved a bicyclist proceeding straight and a motorist turning left (12 percent of all crashes; 9 percent of F/SI crashes). Most of these crashes occurred at unsignalized intersections (25 percent of all crashes; 46 percent of F/SI crashes), followed by signalized intersections (25 percent of all crashes; 36 percent of F/SI crashes). These crashes were most frequently cited as a road user failing to yield to oncoming traffic before making a turn (43 percent of all crashes; 46 percent of F/SI crashes) and failure to making a turn in a safe place and use a turn signal (15 percent of all crashes; 0 F/SI crashes).

The third most common crash type involved a bicyclist proceeding straight and a motorist making a right turn (11 percent of all crashes; 6 percent of F/SI crashes). Most of these crashes occurred at unsignalized intersections (43 percent of all crashes; 29 percent of F/SI crashes) and signalized intersections (32 percent of crashes; 53 percent of F/SI crashes). Note that we have

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incomplete crash types for more than 18 percent of crashes (motorist or bicyclist action “not stated”), which hampers a complete understanding of bicyclist safety in the county.

Table 43: Movement-Based Crash Types, Bicyclist Crashes (2018-2022) Movement-Based Crash Types,
Bicyclists
Count % of
Crashes
# of
F/SI
Crash
% of
F/SI
Crash
EPDO
Score
% of
Total
EPDO
Score
Avg
EPDO
Score
Bike Proceeding Straight, MV
Proceeding Straight 186 21% 21 18% 4,911 18% 26.7
Bike Proceeding Straight, MV Making
Left Turn 106 12% 11 9% 2,705 10% 25.5
Bike Proceeding Straight, MV Making
Right Turn 100 11% 7 6% 2,025 8% 20.3
Bike Not Stated, MV Proceeding
Straight 49 6% 9 8% 1,919 7% 39.2
Bike Proceeding Straight, MV Not
Stated 36 4% 9 8% 1,831 7% 50.9
Bike Making Left Turn, MV Proceeding
Straight 22 3% 5 4% 1,009 4% 45.9
Bike Not Stated, MV Making Left Turn 22 3% 5 4% 1,074 4% 51.1 Bike Proceeding Straight, MV Entering
Traffic 21 2% 2 2% 576 2% 27.4
Bike Not Stated, MV Making Right Turn 18 2% 0 0% 188 1% 10.4 Bike Traveling Wrong Way, MV Making
Right Turn 17 2% 1 <1% 357 1% 21.0
Other 297 34% 48 41% 10,570 40%

36.2 Total 874 100% 118 100% 27,165 100% 31.4
Source: TIMS 2023

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Motor Vehicles
Intersection
Table 44 summarizes frequency and severity for all intersection motor vehicle crashes based on crash type. The predominant crash types are broadside (38 percent), rear end (30 percent) and sideswipe (11 percent). These three make up the majority of total crashes (79 percent) and total F/SI crashes (56 percent). Head-on and hit object account for 17 percent of total crashes but make up 26 percent of F/SI crashes and twenty five percent (24 percent) of the total EPDO score.

The top three intersection crash types accounting for EPDO score – broadside, rear end, and head-on – have the following location characteristics:
◼ Broadside: 61 percent occurred unsignalized intersections (66 percent of EPDO score) ◼ Rear end: 55 percent at occurred at signalized intersections (51 percent of EPDO score) ◼ Head-on: 51 percent at occurred at signalized intersections (59 percent of EPDO score) Table 44: Crash Type and Severity, Motor Vehicle Intersection Crashes Crash Type Count % of 5-
year
Total
Crashes
F/SI
Count
% of Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Broadside 1,471 38% 93 38% 28,167 38% 19.1
Rear End 1,145 30% 30 12% 14,951 20% 13.1
Sideswipe 411 11% 13 5% 5,492 7% 13.4
Head-On 366 10% 45 18% 9,982 14% 27.3
Hit Object 277 7% 40 16% 9,046 12% 32.7
Other 102 3% 8 3% 2,333 3% 22.9
Overturned 48 1% 14 6% 3,018 4% 62.9
Unknown 7 0% 2 1% 285 0% 40.7
Total 3,827 100% 245 100% 73,274 100% 232.0
Source: TIMS 2023

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Roadway
Table 45 summarizes frequency and severity for all roadway motor vehicle crashes based on crash type. The predominant crash types are rear end (29 percent), hit object (21 percent), and broadside (18 percent). Hit object crashes account for disproportionately large 35 percent of F/SI crashes and 30 percent of EPDO score, compared to 21 percent overall.

Table 45: Crash Type and Severity, Motor Vehicle Roadway Crashes Crash Type Count % of 5-
year
Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Rear End 713 29% 32 12% 11,460 18% 16.1
Hit Object 510 21% 92 35% 18,963 30% 37.2
Broadside 437 18% 26 10% 8,074 13% 18.5
Sideswipe 340 14% 23 9% 6,483 10% 19.1
Head-On 241 10% 49 18% 9,960 16% 41.3
Overturned 168 7% 33 12% 6,785 11% 40.4
Other 69 3% 11 4% 2,352 4% 34.1
Unknown 9 0% 0 0% 84 0% 9.3
Total 2,487 100% 266 100% 64,161 100% 215.9
Source: TIMS 2023

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Violations
Pedestrians
Table 46 summarizes pedestrian crashes by the officer-reported violation types. The most common violations Included:
◼ pedestrian right of way (50 percent of all crashes; 37 percent of F/SI crashes) ◼ pedestrian violation (19 percent of all crashes; 30 percent of F/SI crashes) ◼ unknown (5 percent of all crashes; 10 percent of F/SI crashes) For pedestrian right of way crashes, the detailed violation descriptions reflect that the vast majority of these crashes involved a driver who did not yield to a pedestrian at an intersection (96 percent of crashes), followed to a much lesser extent by drivers who did not yield to a pedestrian at a driveway (3 percent of crashes). Most of these crashes occurred at unsignalized intersections (56 percent of all crashes; 65 percent of F/SI crashes), and they tended to be more severe than crashes at signalized intersections.

Most pedestrian violation crashes involved a pedestrian who failed to yield right of way (78 percent of all crashes; 76 percent of F/SI crashes), followed by illegal midblock crossing (9 percent of crashes; 10 percent of F/SI crashes). For these latter sub-violation types (both within pedestrian violation), about one third of crashes resulted in an F/SI outcome.

When looking at lighting conditions for the top two violation types, dark lighting conditions appear to have a weaker association with F/SI crashes for pedestrian right of way, with one-third of F/SI crashes having occurred during dark lighting conditions compared to two-thirds of F/SI crashes for pedestrian violation. This finding may reflect pedestrian crossings at locations that lack safety countermeasures (71 percent of F/SI crashes occurred at unsignalized locations).

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Table 46: Reported Violations, Pedestrian Crashes (2018-2022) Reported Violation Count % of
Crashes
# of F/SI
Crashes
% of
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Avg
EPDO
Score
Pedestrian Right of Way 541 50% 76 37% 17,638 41% 32.7
Pedestrian Violation 198 19% 63 30% 11,375 27% 58.0
Unknown 57 5% 21 10% 3,546 8% 62.2
Unsafe Speed 65 6% 14 7% 2,637 6% 41.9
Improper Turning 55 5% 7 3% 1,644 4% 30.4
Unsafe Starting or
Backing 45 4% 6 3% 1,441 3% 32.0
Traffic Signals and Signs 30 3% 6 3% 1,217 3% 40.6
Driving or Bicycling Under
the Influence of Alcohol
or Drug
14 1% 3 1% 637 2% 45.5
Other Improper Driving 9 1% 3 1% 582 1% 64.7
Not Stated 10 1% 3 1% 548 1% 54.8
Improper Passing 5 1% 2 1% 415 1% 83.0
Other (violation with < 1%
share of EPDO) 44 4% 4 2% 977 2% 22.2
Total 1,073 100% 208 100% 42,657 100% 40.1
Source: TIMS 2023

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Bicyclists
Table 47 summarizes bicyclist crashes by officer-reported violation types. The most frequent violation types included:
◼ automobile right of way (19 percent of crashes; 15 percent of F/SI crashes) ◼ unsafe speed (18 percent of crashes; 28 percent of F/SI crashes) ◼ improper turning (17 percent of crashes; 12 percent of F/SI crashes) Crashes cited as automobile right of way generally involved a road user failing to yield to traffic when entering or crossing a highway/road (32 percent of all crashes), not yielding to oncoming traffic before turning or making a U-turn (30 percent of crashes), and not stopping at a stop sign and yielding to other road users who do not have a stop sign (23 percent of crashes).

The majority of crashes cited as unsafe speed were cited as the bicyclist traveling too fast (83 percent of crashes; 88 percent of F/SI crashes); of these 187 unsafe speed crashes, 101 were solo bicyclist crashes. Most of these 187 crashes occurred at segment locations (55 percent), followed by unsignalized locations (34 percent). Note that these data should be interpreted with caution, as unsafe speed violation type may be used as a blanket violation or a default violation when the proper violation type is not entirely clear from the responding officer’s perspective.

Table 47: Reported Violations, Bicyclist Crashes (2018-2022) Reported Violation Count % of
Crashes
# of F/SI
Crashes
% of
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Avg
EPDO
Score
Unsafe Speed 187 18% 49 28% 10,005 26% 53.5
Automobile Right of
Way 199 19% 27 15% 6,392 17% 32.3
Improper Turning 180 17% 21 12% 5,068 13% 28.3
Traffic Signals and Signs 95 9% 18 10% 3,441 9% 37.4
Other Hazardous
Violation 66 6% 14 8% 2,754 7% 43.0
Unknown 53 5% 13 7% 2,615 7% 50.3
Wrong Side of the Road 118 11% 6 3% 2,144 5% 18.2
Other Than Driver (or
Pedestrian) 21 2% 7 4% 1,350 4% 67.5
Driving or Bicycling
Under the Influence of
Alcohol or Drug
13 1% 6 3% 1,114 3% 85.7
Not Stated 24 2% 3 2% 753 2% 31.4
Improper Passing 38 4% 2 1% 706 2% 18.6
Unsafe Starting or
Backing 18 1% 3 2% 686 2% 38.1
Other (violation with <
1% share of EPDO) 33 3% 3 2% 740 2% 22.4
Total 1,067 100% 175 100% 38,372 100% 36.3

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Motor Vehicles
Table 48 summarizes frequency and severity for motor vehicle crashes based on primary collision factors. The predominant crash types are unsafe speed (27 percent), automobile right of way (19 percent), and improper turning (18 percent) make up the majority of total crashes (46 percent) and total F/SI crashes (58 percent).

Table 48. Primary Collision Factors - Motor Vehicles
Primary Collision Factor Count % of
Crashes
Count of
F/SI
Crashes
% of F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Unsafe Speed 1,732 27% 118 23% 33,985 25% 19.6
Automobile Right of
Way 1,177 19% 61 12% 20,581 15% 17.5
Improper Turning 1,108 18% 97 19% 25,170 18% 22.7
Traffic Signals and
Signs 632 10% 41 8% 11,597 8% 18.3
Driving or Bicycling
Under the Influence
of Alcohol or Drug
598 9% 95 18% 20,366 15% 34.1
Following Too
Closely 206 3% 3 1% 2,470 2% 12.0
Unsafe Starting or
Backing 179 3% 9 2% 2,874 2% 16.1
Unknown 140 2% 15 3% 3,063 2% 21.9
Wrong Side of the
Road 136 2% 32 6% 6,002 4% 44.1
Other Than Driver (or
Pedestrian) 100 2% 16 3% 3,480 3% 34.8
Other 320 5% 28 5% 7,229 5% 22.6
Total 6,328 100% 515 100% 136,817 100% 21.6
Source: TIMS 2023

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Alcohol/Drug Usage
This section analyzes alcohol/drug usage as a reported factor in crashes.

Pedestrians
Table 49 summarizes pedestrian crashes by whether the driver or pedestrian was under the influence of alcohol. The vast majority of crashes did not have any road user found to be under the influence of alcohol (96 percent of all crashes; 94 percent of F/SI crashes). Crashes were much more likely to result in a F/SI if the pedestrian was reported to be under the influence of alcohol, with 35 percent of crashes resulting in F/SI compared to 19 percent for crashes in which no one was found to be under the influence of alcohol. Additionally, 10 of the 12 F/SI crashes with one of the roadway users under the influence occurred during dark lighting conditions.

Table 49: Under the Influence of Alcohol, Pedestrian Crashes (2018-2022) Driver
under the
Influence
Pedestrian
under the
Influence
Count % of Crashes # of F/SI
Crashes
% of
F/SI
Crashes
EPDO Score % of
Total
EPDO
Score
Average
EPDO
Score
No No 1,033 96% 196 94% 40,278 94% 39.3
No Yes 23 2% 8 4% 1,529 4% 66.5
Yes No 17 2% 4 2% 850 2% 50.0
Total 1,073 100% 208 100% 42,657 100% 40.1
Source: TIMS 2023

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Bicyclists
Table 50 summarizes bicyclist crashes by whether the driver or bicyclist was under the influence of alcohol. Like pedestrian crashes, few reported crashes identified either the motorist or bicyclist as being under the influence of alcohol (1.8 percent of all crashes; 4.6 percent of F/SI crashes).

Table 50: Under the Influence of Alcohol, Bicyclist Crashes (2018-2022) Driver
under the
Influence
Bicyclist
under
the
Influence
Count % of Crashes # of F/SI
Crashes
% of
F/SI
Crashes
EPDO Score % EPDO
Score
Average
EPDO
Score
No No 1,047 98% 167 95% 36,923 96% 35.6
No Yes 11 1% 4 2% 713 2% 64.8
Yes No 9 1% 4 2% 736 2% 81.8
Total 1,067 100% 175 100% 38,372 100% 36.3
Source: TIMS 2023
Motor Vehicles
Table 51 summarizes frequency and severity for all motor vehicle crashes based on alcohol/drug usage being report. Drivers were recorded as under the influence in 10 percent of total crashes but 19 percent of F/SI crashes.

Table 51: Alcohol/Drug Usage Crashes, Motor Vehicles
Alcohol/Drug
Usage
Reported?
Count % of 5-
year Total
Crashes
F/SI
Count
% of
Total
F/SI
Crashes
EPDO
Score
% of
Total
EPDO
Score
Average
EPDO
Score
Yes
606 10% 97 19% 20,731 15% 34.2
No 5,718 90% 418 81% 117,268 85% 20.5
Total 6,324 100% 515 100% 137,999 100% 21.8
Source: TIMS 2023

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Solo Bicyclist Crashes
Table 52 summarizes crashes for solo bicyclist crashes and crashes that involved both a bicyclist and a motorist. Given the relatively high share of F/SI crashes that solo bicyclist crashes represent (only 18 percent of all crashes, but 33 percent of F/SI crashes), this section aims to see if there are distinct patterns between the two crash types.

Table 52: Solo Bicyclist Crashes (2018-2022)
Mode Type Bicyclist
Crashes
% of
Crashes
# of
F/SI
Crashe
s
% of
F/SI
Crashe
s
EPDO
Score
% of
Total
EPDO
Score
Averag
e EPDO
Score
Bicyclist-Motorist 874 82% 118 67% 27,165 14% 31.4
Solo bicyclist 193 18% 57 33% 11,207 30% 58.4
Total 1,067 100% 175 100% 38,372 17% 36.3
Source: TIMS 2023
Violation type
The reported violation types are distinct for the two crash types. The majority of solo bicyclist crashes were cited as unsafe speed for all crashes and F/SI crashes. For bicyclist-motorist crashes, in contrast, the most frequent violation type was automobile right of way. The distribution of crashes by violation for each crash type also differs unsafe speed had a substantially larger proportion of crashes (52 percent) for solo bicyclist crashes, while automobile right of way accounted for a much smaller share of bicyclist-motorist crashes (23 percent). It is highly unlikely that a solo bicyclist crash would have an automobile right of way violation, but officers may code this while interpreting a bicyclist as an automobile user who they deem to have violated right of way.
◼ Solo bicyclist
– Unsafe Speed: 52 percent of all crashes; 56 percent of F/SI crashes – Improper turning: 18 percent of all crashes; 14 percent of F/SI crashes – Other than Driver or Pedestrian: 10 percent of all crashes; 12 percent of F/SI crashes ◼ Bicyclist-Motorist
– Automobile right of way: 23 percent of all crashes; 23 percent of F/SI crashes – Improper turning: 17 percent of all crashes; 11 percent of F/SI crashes – Wrong side of the road: 13 percent of all crashes; 5 percent of F/SI crashes

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Location Type
Solo bicyclist crashes occur more frequently at segment locations (55 percent) compared to bicyclist-motorist crashes (23 percent), although it is the unsignalized intersection crashes that are disproportionately severe for solo bicyclists. Solo bicyclist segment crashes had a much larger share of crashes that had some kind of roadway condition issue (20 percent) compared to bicyclist-motorist crashes (2 percent). While bicyclists are particularly vulnerable to injury with involved in a crash with a motorist, they are also vulnerable to poor roadway conditions, which can cause the bicyclist to fall or lost control while making an evasive maneuver. ◼ Solo bicyclist
– segment: 55 percent of all crashes; 54 percent of F/SI crashes – unsignalized intersection: 33 percent of all crashes; 39 percent of F/SI crashes – signalized intersection: 11 percent of all crashes; 7 percent of F/SI crashes ◼ Bicyclist-Motorist
– segment: 23 percent of all crashes; 38 percent of F/SI crashes – unsignalized intersection: 45 percent of all crashes; 40 percent of F/SI crashes – signalized intersection: 23 percent of all crashes; 22 percent of F/SI crashes Functional Classification
A key difference between the two crash types is that solo bicyclists crashes, and particularly F/SI crashes, occurred less frequently along or at primary roadways. Aside from crashes along or at primary roadways , the distribution of crashes by functional classification are similar, and both crash types most frequently occurred along secondary streets. ◼ Solo bicyclist
– Primary: 13 percent of all crashes; 5 percent of F/SI crashes – Secondary: 40 percent of all crashes; 44 percent of F/SI crashes – Tertiary: 17 percent of all crashes; 21 percent of F/SI crashes – Residential: 18 percent of all crashes; 18 percent of F/SI crashes ◼ Bicyclist-Motorist
– Primary: 20 percent of all crashes; 22 percent of F/SI crashes – Secondary: 36 percent of all crashes; 35 percent of F/SI crashes – Tertiary: 18 percent of all crashes; 17 percent of F/SI crashes – Residential: 19 percent of all crashes; 16 percent of F/SI crashes Posted Speed Limit
Solo bicyclist crashes occurred more often along 45mph streets than bicyclist-motorist crashes, although they were more likely to be severe on 30-35 mph streets. ◼ Solo bicyclist
– 25 mph: 25 percent of all crashes; 30 percent of F/SI crashes – 30-35 mph: 21 percent of all crashes; 30 percent of F/SI crashes Page 779 of 1188 February 5, 2024 Page 91
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– 40-45 mph: 42 percent of all crashes; 32 percent of F/SI crashes – 50+ mph: 5 percent of all crashes; 4 percent of F/SI crashes ◼ Bicyclist-Motorist
– 25 mph: 32 percent of all crashes; 25 percent of F/SI crashes – 30-35 mph: 31 percent of all crashes; 32 percent of F/SI crashes – 40-45 mph: 32 percent of all crashes; 33 percent of F/SI crashes – 50+ mph: 3 percent of all crashes; 7 percent of F/SI crashes Unincorporated Areas
Solo bicyclist crashes had nearly three times the share of crashes that occurred in unincorporated areas of the county. Many of the solo bicyclist crashes in unincorporated areas are clustered along Skyline Blvd, Tunitas Creek Rd, Kings Mountain Rd, Half Moon Bay Rd, and Cañada Rd. Most of these streets are narrow, have narrow or no shoulders, and are popular recreation bicycling routes.
◼ Solo bicyclist
– Unincorporated: 33 percent of all crashes; 30 percent of F/SI crashes ◼ Bicyclist-Motorist
– Unincorporated: 12 percent of all crashes; 20 percent of F/SI crashes

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Demographic Party and Victim Variables
This section provides analysis of the victims compared for all crashes as well as specifically for fatal and severe injury (F/SI) outcomes for different age groups, sex, and racial groups. Analyzing these characteristics helps identify any disparities and inequities in road safety. In addition, it becomes possible to better understand the factors contributing to crashes and their outcomes.

Different age groups, sex, and racial groups may have varying and intersecting risk factors, behaviors and vulnerabilities that contribute to crashes. This insight can inform the County as well as specific jurisdictions towards development and implementation of effective safety measures to specific groups, including engineering and operational improvements, as well as policies, programs, and targeted educational campaigns aligned with the Safe System Approach.

The analysis looks into the victim characteristics by their mode of travel (motor vehicle drivers/occupants, pedestrians, and bicyclists). This comparison is based on the number of victims, and not the number of crashes, so the total numbers are different than in the other analyses within this memo. Any given crash may injure multiple victims, at different levels of severity. The relationship among crashes, parties, and victims is explained in Crash Data Overview on page 20.

Analyzing the victim data provides additional insight into these crashes and potential crash dynamics. This analysis compared the distribution of parties involved in crashes to the population distribution of San Mateo County. Values greater than one suggest that a certain segment of the population is overrepresented on a per capita basis, while values less than one suggest that that segment of the population is underrepresented on the same basis. It is important to note that this comparison is imperfect in two ways. First, if more or fewer people from a segment of the population bicycle, walk, or drive, we would expect that to be reflected in crash rates, all else equal – and this proportion of people who bicycle, walk, or drive may not reflect their per capita proportion. We likely see this, for example, in trends related to age and sex, and potentially related to race. In the absence of more nuanced exposure data, however, a per capita understanding is still valuable to help us understand how crashes are distributed among various segments of the population. Second, the home zip code is not readily available for all parties involved in the crash, so we cannot rule out that some people riding a bicycle or driving a motor vehicle live outside of San Mateo and their inclusion will therefore marginally affect the accuracy of the victim-to-population ratio. This effect is more likely to apply to drivers (who have longer travel distances on average) than to bicyclists or pedestrians in the county.

Age
Pedestrians
Table 53 summarizes pedestrian victims involved in a crash by age. Pedestrian victims aged between 15 to 29 and 55 to 59 years of age accounted for the largest share of victims for all injury severities. However, pedestrian victims aged from 40 to 44 and 55 to 74 years of age had the largest share of F/SI victims. This is likely a reflection of the level of force the human body can endure as people age, such that pedestrian victims aged in their twenties are less likely to experience a severe injury than a victim aPage 781 of 1188 ged in their seventies. Indeed, research has found that February 5, 2024 Page 93
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an injury to a 65-year-old is approximately the same the injury a 30-year old would experience at 11 mph higher14. This is also reflected in the F/SI victim-to-population ratios which show victims aged 65 or older were the most overrepresented age cohorts, whereas younger populations were generally underrepresented in F/SI crashes. Furthermore, younger populations between 15 to 19 years of age were the most overrepresented victims but fairly underrepresented in F/SI crashes.

Table 53: Victim age breakdown, Pedestrian crashes (2018-2022) Age Victims F/SI Victims % of
Victims
% of
F/SI
Victims
Share of
Population15
Victims:

Population
Ratio
F/SI
Victims:

Population
Ratio
0 - 4 18 2 2% 1% 6% 0.3 0.2
5 - 9 25 5 2% 2% 6% 0.4 0.4
10 - 14 75 11 7% 5% 6% 1.2 0.9
15 - 19 94 8 9% 4% 5% 1.6 0.7
20 - 24 79 11 7% 5% 5% 1.4 1.0
25 - 29 91 13 8% 6% 7% 1.2 0.9
30 - 34 76 10 7% 5% 8% 0.9 0.6
35 - 39 72 13 7% 6% 8% 0.9 0.8
40 - 44 63 19 6% 9% 7% 0.9 1.3
45 - 49 65 13 6% 6% 7% 0.9 0.9
50 - 54 68 11 6% 5% 7% 0.9 0.8
55 - 59 100 19 9% 9% 7% 1.3 1.3
60 - 64 69 18 6% 9% 6% 1.0 1.4
65 - 69 72 19 7% 9% 5% 1.3 1.7
70 - 74 63 19 6% 9% 4% 1.5 2.3
75 - 79 30 8 3% 4% 3% 1.0 1.4
14 Tefft, Brian C. 2013. Impact speed and a pedestrian's risk of severe injury or death. Accident Analysis & Prevention, 50: 871-878. https://doi.org/10.1016/j.aap.2012.07.022. 15 Based on ACS 5-year estimates Page 782 of 1188
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Age Victims F/SI Victims % of
Victims
% of
F/SI
Victims
Share of
Population15
Victims:

Population
Ratio
F/SI
Victims:

Population
Ratio
80 - 84 25 10 2% 5% 2% 1.2 2.5
85 + 0 0 0% 0% 2% 0.0 0.0
Unknown 31 5 3% 2%
Total 1085 209 100% 100% 100% 1.0 1.0
Source: TIMS 2023, American Community Survey 2021 5-year tables Bicyclists
Table 54 summarizes bicyclist victims involved in a crash by age. Younger victims aged between 10 to 19 years of age and victims aged between 50 to 54 accounted for the largest share of victims involved in a crash; these cohorts are also some of the most overrepresented victims in overall crashes. In contrast, victims aged at least 70 were the most underrepresented victims, which likely reflects bicycling exposure amongst various age groups. Younger victims aged between 10 to 19 were neither over nor underrepresented in F/SI crashes in these data.

However, victims aged between 45 to 64 accounted for the largest share of F/SI victims, and victims aged between 45 to 54, 60 to 64, and 75 to 79 were the most overrepresented age cohorts in terms of fatal or severe injury.

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Table 54: Victim age breakdown, Bicyclist crashes (2018-2022) Age Victims F/SI
Victims
% of
Victims
% of F/SI
Victims
Share of
Population16
Victims:

Population
Ratio
F/SI
Victims:

Population
Ratio
0 - 4 1 0 <1% 0.0% 5.5% 0.0 0.0
5 - 9 18 1 2% 0.6% 5.7% 0.3 0.1
10 - 14 120 11 11% 6.2% 5.8% 1.9 1.1
15 - 19 116 9 11% 5.1% 5.4% 2.0 0.9
20 - 24 68 8 6% 5% 5% 1.2 0.9
25 - 29 90 13 8% 7% 7% 1.2 1.1
30 - 34 79 8 7% 5% 8% 0.9 0.6
35 - 39 64 12 6% 7% 8% 0.8 0.9
40 - 44 71 11 7% 6% 7% 1.0 0.9
45 - 49 77 19 7% 11% 7% 1.0 1.5
50 - 54 121 27 11% 15% 7% 1.6 2.2
55 - 59 79 11 7% 6% 7% 1.1 0.9
60 - 64 72 20 7% 11% 6% 1.1 1.8
65 - 69 56 11 5% 6% 5% 1.0 1.2
70 - 74 28 6 3% 3% 4% 0.6 0.8
75 - 79 15 8 1% 5% 3% 0.5 1.7
80 - 84 9 2 1% 1% 2% 0.4 0.6
85 + 0 0 0% 0% 2% 0.0 0.0
Unknown 4 0 0% 0%
Total 1084 177 100% 100% 100.0% 1.0 1.0
Source: TIMS 2023, ACS 2021 5-year tables
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Motor Vehicles
Table 55 compares the motor vehicle victim age breakdown against the age breakdown of residents in San Mateo County. Values greater than one indicate that a given age group is overrepresented in the crash data.

The table shows that victims between the ages of 10 and 29 are more likely to be injured, including F/SI as result of traffic safety issues on average than other groups. Victims between the ages 15 to 24 are especially over-represented with victims and F/SI ratios over two.

Local safety plan review identified children, young adults, and older adults as target victim age populations.

Victims ranging from ages 0-14 (children) account for 18 percent of the countywide population and 14 percent of total crashes and 8 percent of F/SI motor vehicle crash victims. Victims ranging from ages 15-24 (young adult) account for 11 percent of the countywide population but account for 22 percent of total crashes and 26 percent of F/SI motor vehicle crash victims.

Victims ranging from ages 65 and up account for 16 percent of the countywide population but account for 10 percent of total crashes and 14 percent of F/SI motor vehicle crash victims.

Table 55: Victim age breakdown, Motor Vehicle Only crashes (2018-2022) Age Victims % of Victims F/SI
Victims
% of
F/SI
Victims
Share of
Population17
Victims:

Population
Ratio
F/SI Victims:

Population
Ratio
0 - 4 271 3% 2 0% 5% 0.5 0.1
5 - 9 315 3% 6 1% 6% 0.5 0.2
10 - 14 854 8% 41 7% 6% 1.4 1.2
15 - 19 1,140 11% 73 12% 5% 2.0 2.3
20 - 24 1,090 11% 81 14% 5% 2.0 2.6
25 - 29 951 9% 71 12% 7% 1.3 1.7
30 - 34 832 8% 39 7% 8% 1.0 0.9
35 - 39 703 7% 32 5% 8% 0.9 0.7
40 - 44 669 6% 37 6% 7% 1.0 0.9
45 - 49 643 6% 45 8% 7% 0.9 1.1
50 - 54 637 6% 40 7% 7% 0.9 1.0
17 Based on ACS 5-year estimates Page 785 of 1188
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Age Victims % of Victims F/SI
Victims
% of
F/SI
Victims
Share of
Population17
Victims:

Population
Ratio
F/SI Victims:

Population
Ratio
55 - 59 326 3% 3 1% 7% 0.5 0.1
60 - 64 511 5% 33 6% 6% 0.8 0.9
65 - 69 414 4% 35 6% 5% 0.8 1.1
70 - 74 312 3% 16 3% 4% 0.7 0.7
75 - 79 204 2% 16 3% 3% 0.7 1.0
80 - 84 130 1% 9 2% 2% 0.7 0.8
85 + 15 <1% 0 0% 2% 0.1 -
Unknown 353 3% 13 2% 0% N/A N/A
Total 10,370 100% 592 100% 100% 1.0 1.0
Source: TIMS 2023, ACS 2021 5-year tables
Table 56 compares the driver’s age breakdown against the age breakdown of residents in San Mateo County. Values greater than one indicate that a given age group is over-represented in the crash data. Drivers summarized in this table are the drivers involved in all crashes (pedestrian, bicyclists, motor vehicle only, solo-motor vehicle, etc.). This table also summarizes the number and share of driver who were involved in F/SI crashes, in which the driver may not have sustained a F/SI but rather a pedestrian or bicyclist may have been fatally or severely injured.

The table shows that drivers between the ages of 20 and 44 are disproportionately likely (on a per-capita basis) to be involved in a crash, and drivers aged between 20 and 34 are overrepresented in F/SI crashes compared to other groups. Drivers between the ages 20 to 24 are especially over-represented in crashes and F/SI ratios.

The table also shows that the aging population is generally underrepresented in both overall crashes and F/SI crashes. Additional research is needed to better reflect travel behaviors within the region, as these results may indicate that younger populations drive more frequently, more recklessly, and/or during higher-risk times (e.g., when visibility is limited (darkness)) compared to the aging population.

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Table 56: Driver age breakdown, All crashes (2018-2022) Driver Age #
Drivers
%
Drivers
# Drivers
in F/SI
Crashes
# Drivers in
F/SI
Crashes
Share of
Population18
Driver:

Population
Ratio
F/SI Driver:

Population
Ratio
0 - 4 1 0.0% 0 0.0% 5% 0.00 0.00
5 - 9 1 0.0% 0 0.0% 6% 0.00 0.00
10 - 14 12 0.2% 0 0.0% 6% 0.03 0.00
15 - 19 351 5.6% 32 5.3% 5% 1.03 0.98
20 - 24 750 11.9% 76 12.7% 5% 2.25 2.39
25 - 29 777 12.4% 84 14.0% 7% 1.78 2.01
30 - 34 673 10.7% 69 11.5% 8% 1.38 1.48
35 - 39 636 10.1% 46 7.7% 8% 1.31 0.99
40 - 44 522 8.3% 39 6.5% 7% 1.22 0.96
474 7.5% 41 6.8% 7% 1.08 0.98
50 - 54 483 7.7% 51 8.5% 7% 1.13 1.25
55 - 59 480 7.6% 43 7.2% 7% 1.11 1.04
60 - 64 356 5.7% 39 6.5% 6% 0.91 1.05
65 - 69 301 4.8% 33 5.5% 5% 0.90 1.04
70 - 74 233 3.7% 19 3.2% 4% 0.92 0.79
75 - 79 151 2.4% 17 2.8% 3% 0.89 1.05
80 - 84 85 1.4% 11 1.8% 2% 0.72 0.97
85 + 0 0.0% 0 0.0% 2% 0.00 0.00
Unknown 31 0.5% 5 0.8% 0%
Total 6286 100.0% 600 100.0% 100% 1.00 1.00
Source: TIMS 2023, ACS 2021 5-year tables
18 Based on ACS 5-year estimates Page 787 of 1188
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Sex
Disclaimer: Party sex is based on officer’s assumption or visual impression, which can be problematic and inaccurate. The only categorical values for sex in the crash report form include “male”, “female”, and “Not Stated” and do not include other sex identities. The party representation and comparison made to the San Mateo county population should be interpreted with caution given these reporting shortcomings.

Table 57 compares the victim sex breakdown against the sex breakdown of residents in San Mateo County. To compare these distributions, the percentage of victims and of F/SI victims within each sex divided by the percentage share in the population overall. Values greater than one indicate that a given sex is over-represented in the crash data.

The table shows that male victims are more likely to be injured including F/SI as result of traffic safety issues on average than other victims.

Table 57: Victim sex breakdown (2018-2022)
Sex Victims % of
Victims
F/SI
Victims
% of F/SI
Victims
Share of
Population19
Victims:

Populations
Ratio
F/SI
Victims:

Population
Ratio
Male 6,829 53% 666 67% 50% 1.1 1.3
Female 5,915 46% 319 32% 50% 0.9 0.6
Other 139 1% 3 <1% 0% N/A N/A
Not
Stated
1 <1% 0 0% 0% N/A N/A
Total 12,884 100% 988 100% 100% 1.0 1.0
Source: TIMS 2023, ACS 2021 5-year tables
Table 58 shows fatally and severely injured victims by sex and mode. Male victims are more overrepresented in F/SI outcomes than in all crashes—67 percent F/SI victims—and account for approximately three times as many F/SI bicyclist and driver victims. 19 Based on ACS 5-year estimates. Page 788 of 1188
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Table 58: Fatally and severely injured victims by sex and mode (2018-2022) Victim Type Male Female Other Not Stated
F/SI
Victims
% of
F/SI
Victims
F/SI
Victims
% of
F/SI
Victims
F/SI
Victims
% of F/SI
Victims
F/SI
Victims
% of
F/SI
Victims
Bicyclist 144 15% 33 3% - 0% - -
Driver 351 36% 121 12% 1 <1% - -
Other 4 - 2 - - 0% - -
Passenger 48 5% 69 7% 1 <1% - -
Pedestrian 119 12% 94 10% 1 <1% - -
Total 666 67% 319 32% 3 <1% - -
Source: TIMS 2023,
Race
Disclaimer: Party race is based on an officer’s assumption or visual impression, which can be problematic and inaccurate. Additionally, there are only five racial categories (excluding “Not Stated”) within the crash data, in contrast to the American Community Survey (ACS), which has nearly twice as many race and ethnicity categories. The party representation and comparison made to the San Mateo county population should be interpreted with caution given these reporting shortcomings.

Victims are assessed based on race to identify whether any racial groups are substantially overrepresented. The police report data encodes victim races as “Asian”, “Black”, “Hispanic”, “Other”, and “White”, based solely on the reporting officer’s observation and best judgment. 20 This information is not included on personal identification unlike sex. This method of reporting inevitably leads to inaccurate classification of some victims. In addition, these racial classifications do not cover the full range of ethnic and racial groups that individuals are able to self-report in the context of the ACS, so a direct comparison to the distribution within the population is not possible. Despite this, we used our best judgment to connect the victim racial breakdown to the overall population, as presented in Table 59. The findings and comparisons should be interpreted with caution. Lastly, race information is recorded in crash data at the party level rather than at the victim level, so the total numbers will differ from the age and sex findings previously listed. For example, if a car has a driver and two passengers, then all three people are included in the victims table but only the driver is included in the party table: hence, there is no passenger information in the subsequent tables. 20 California Highway Patrol. “Collision Investigation Manual.” 2003. Page 789 of 1188 February 5, 2024 Page 101
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These results suggest that Hispanic, Black, and “Other” victims are disproportionately victims of traffic crashes, and that these groups are disproportionately killed or severely injured. However, this table does not reflect how people travel in San Mateo County, e.g., if people in one race drive significantly more than those of another race, and therefore cannot provide a true picture of traffic risk. Additionally, it bears repeating that the reporting method is likely error-prone; thus, any conclusions drawn from this section should be caveated with recognition of a need for further research.

Table 59 clearly shows an overrepresentation of Black and Hispanic victims in crashes, at rates

2.2 time and 1.2 times their share of the population. This disproportionality may be due to several factors, including historic and current underinvestment in pedestrian and bicyclist infrastructure in predominantly minority neighborhoods, underinvestment in education campaigns in these neighborhoods, and the percentages of residents in these neighborhoods that walk, use transit, and/or bicycle for transportation, and interactions between these factors.

Victims identified as “Other” races may also be overrepresented, though again it is not clear what the makeup of that category is among crash victims compared to the ACS categories.

Table 59: Victim racial breakdown (2018-2022)
Race Victims F/SI
Victims
% of
Victims21
% of F/SI
Victims
Share of
Population22
Victims:

Populations
Ratio
F/SI
Victims:

Population
Ratio
White 5,243 521 30% 36% 38% 0.8 0.9
Hispanic 4,911 366 28% 25% 24% 1.2 1.1
Not
Stated
3,489 270 20% 19% 0% N/A N/A
Other 1,487 114 9% 8% 5% 1.7 1.6
Asian 1,371 127 8% 9% 31% 0.3 0.3
Black 760 61 4% 4% 2% 2.2 2.1
Total 17,261 1,459 100% 100% 100% 1.0 1.0
Source: TIMS 2023, ACS 2021 5-year tables
In addition to looking at the overall burden of traffic crashes by race, we can consider how the fatally and severely injured victims of each race are impacted while traveling in various ways, as depicted in Table 60. White and Hispanic travelers are the most likely to be severely injured or 21 Percentages are assessed based on total number of victims with a stated race. 22 Based on ACS 5-year estimates. Page 790 of 1188
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killed while walking, and that White and Hispanic travelers are the most likely to be severely injured or killed while riding a bicycle.

Over half of bicyclist F/SI victims are white. That is higher than their 38 percent share of the population and commensurate 37 percent share of drivers fatally or severely injured, though we do not have an adequate understanding of the demographics of overall biking in the County for comparison. Likewise, 30 percent of pedestrians fatally or severely injured were Hispanic, which is 1.25 times higher than their 24 percent share of the population.

Table 60: Fatally and severely injured victims by race and mode (2018-2022) Victim
Type
Asian Black Hispanic Other White Not
Stated
Total
# % # % # % # # # # # % # %
Bicyclist 19 1% 8 1% 27 2% 13 1% 104 7% 10 1% 181 13%
Driver 86 6% 41 3% 280 19% 91 6% 355 24% 110 8% 963 66% Other 0 0% 0 0% 0 0% 0 0% 4 <1% 119 8% 123 8%
Pedestrian 22 2% 12 1% 58 4% 10 1% 58 4% 31 2% 191 14%
Total 127 9% 61 4% 366 25% 114 8% 521 36% 270 19% 1,459 100% Share of
Population
31% 2% 24% 5% 38% 0% 100%
Source: TIMS 2023, ACS 2021 5-Year Tables

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JURISDICTION-SPECIFIC ANALYSIS
The descriptive analysis entails findings from a County-level analysis where the number of crashes is suitably large to establish robust and jurisdiction specific findings. This section provides supplemental findings which are specific to the 9 jurisdictions that do not have a safety plan.

Each section starts with top three of the following crash characteristics as observed:
◼ Crash Location
◼ Crash Type
◼ Functional Classification
◼ Lighting Conditions
◼ Posted Speed Limit
◼ Pre-Crash Movement
◼ Relative Direction of Travel
◼ Violation Type
Following this, the analysis identified and lists the top three HIN intersections by mode identified for all 9 jurisdictions. Table 61 lists the number of reported crashes for the nine jurisdictions and the percentage share of crashes that involved pedestrians and bicyclists.

Table 61: Crashes by Severity, Mode, and Jurisdiction
Jurisdiction Severity Level Number of
Reported
Crashes
Pedestrian
Share
Bicyclist
Share
Belmont All Injury/Fatal 244 10.7% 20.7%
F/SI 8 12.5% 25.0%
Brisbane All Injury/Fatal 69 11.6% 7.2%
F/SI 12 50.0% 16.7%
East Palo Alto All Injury/Fatal 458 12.2% 16.4%
F/SI 48 27.1% 20.8%
Foster City All Injury/Fatal 150 13.3% 10.7%
F/SI 13 46.2% 23.1%
Half Moon Bay All Injury/Fatal 145 9.0% 10.3%
F/SI 17 17.6% 11.8%
Hillsborough All Injury/Fatal 38 10.5% 18.4%
F/SI 5 20.0% 40.0%

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Jurisdiction Severity Level Number of
Reported
Crashes
Pedestrian
Share
Bicyclist
Share
Portola Valley All Injury/Fatal 39 7.7% 64.1%
F/SI 14 0.0% 85.7%
San Carlos All Injury/Fatal 281 13.5% 14.2%
F/SI 32 28.1% 12.5%
Woodside All Injury/Fatal 185 0.5% 38.9%
F/SI 44 2.3% 47.7%

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Belmont
The following tables show crash statistics for key variables, locations along the modal HINs, and top intersections based on EPDO. The intersection table is shared for informational purposes and does not represent a potential project list, which will be evaluated and developed as part of a subsequent task. The modal HINs can be viewed dynamically online at this site. 23 Table 62: Crash Statistics for Key Variables, Belmont Variable Rank Pedestrian Bicyclist Motorists
All Crashes 26 Crashes, 1 F/SI
Crashes
35 Crashes, 2 F/SI
Crashes
168 Crashes, 1 F/SI
Crashes
Crash
Location
1 Segment
(11 Crashes, 1 F/SI
Crashes)
Unsignalized
Intersection
(17 Crashes, 1 F/SI
Crashes)
Segment
(72 Crashes, 3 F/SI
Crashes)
2 Unsignalized
Intersection
(13 Crashes, 0 F/SI
Crashes)
Segment
(10 Crashes, 1 F/SI
Crashes)
Unsignalized
Intersection
(70 Crashes, 2 F/SI
Crashes)
3 Signalized
Intersection
(2 Crashes, 0 F/SI
Crashes)
Signalized Intersection
(8 Crashes, 0 F/SI
Crashes)
Signalized Intersection
(41 Crashes, 0 F/SI
Crashes)
Crash Type
1 Perpendicular – Ped
Crossing Not in
Crosswalk, MV
Proceeding Straight
(5 Crashes, 1 F/SI
Crashes)
Same Direction -
Other
(5 Crashes, 1 F/SI
Crashes)
Broadside
(52 Crashes, 4 F/SI
Crashes)
2 Perpendicular - Other
(4 Crashes, 0 F/SI
Crashes)
Solo - Other
(2 Crashes, 1 F/SI
Crashes)
Rear End
(75 Crashes, 0 F/SI
Crashes)
3 Perpendicular - Ped
Crossing in Crosswalk
at Intersection, MV
Making Right Turn
(3 Crashes, 0 F/SI
Crashes)
Same Direction- Bike
Proceeding Straight,
MV Proceeding
Straight
(5 Crashes, 0 F/SI
Crashes)
Sideswipe
(23 Crashes, 1 F/SI
Crashes)
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Variable Rank Pedestrian Bicyclist Motorists
Functional
Classification
1 primary
(4 Crashes, 1 F/SI
Crashes)
primary
(8 Crashes, 1 F/SI
Crashes)
secondary
(81 Crashes, 3 F/SI
Crashes)
2 secondary
(10 Crashes, 0 F/SI
Crashes)
alley
(1 Crashes, 1 F/SI
Crashes)
primary
(43 Crashes, 1 F/SI
Crashes)
3 residential
(8 Crashes, 0 F/SI
Crashes)
secondary
(13 Crashes, 0 F/SI
Crashes)
residential
(37 Crashes, 0 F/SI
Crashes)
Lighting
Condition
1 Light
(18 Crashes, 1 F/SI
Crashes)
Light
(30 Crashes, 2 F/SI
Crashes)
Light
(135 Crashes, 5 F/SI
Crashes)
2 Dark
(8 Crashes, 0 F/SI
Crashes)
Dark
(5 Crashes, 0 F/SI
Crashes)
Dark
(47 Crashes, 0 F/SI
Crashes)
3 Unknown
(1 Crashes, 0 F/SI
Crashes)
Posted
Speed Limit
1 35
(8 Crashes, 1 F/SI
Crashes)
<=25
(8 Crashes, 1 F/SI
Crashes)
35 (55 Crashes, 3 F/SI
Crashes)
2 <=25
(8 Crashes, 0 F/SI
Crashes)
45 (3 Crashes, 1 F/SI
Crashes)
40 (33 Crashes, 2 F/SI
Crashes)
3 30
(7 Crashes, 0 F/SI
Crashes)
35 (15 Crashes, 0 F/SI
Crashes)
<=25
(40 Crashes, 0 F/SI
Crashes)
Pre-Crash
Movement 1
Ped Crossing Not in
Crosswalk, MV
Proceeding Straight
(6 Crashes, 1 F/SI
Crashes)
Other
(11 Crashes, 2 F/SI
Crashes)
Other
(50 Crashes, 1 F/SI
Crashes)

February 5, 2024 Page 107
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Variable Rank Pedestrian Bicyclist Motorists
2 Other
(7 Crashes, 0 F/SI
Crashes)
Bike Proceeding
Straight, MV
Proceeding Straight
(8 Crashes, 0 F/SI
Crashes)
MC Proceeding
Straight, MV Entering
Traffic
(2 Crashes, 2 F/SI
Crashes)
3 Ped Crossing in
Crosswalk at
Intersection, MV
Making Left Turn
(3 Crashes, 0 F/SI
Crashes)
Bike Proceeding
Straight, MV Making
Right Turn
(5 Crashes, 0 F/SI
Crashes)
MV Proceeding
Straight, MV Stopped
(35 Crashes, 0 F/SI
Crashes)
Relative
Direction of
Travel
1 Perpendicular
(19 Crashes, 1 F/SI
Crashes)
Same Direction
(14 Crashes, 1 F/SI
Crashes)
Same Direction
(94 Crashes, 2 F/SI
Crashes)
2 Same Direction
(5 Crashes, 0 F/SI
Crashes)
Solo
(4 Crashes, 1 F/SI
Crashes)
Perpendicular
(41 Crashes, 2 F/SI
Crashes)
3 Unknown
(1 Crashes, 0 F/SI
Crashes)
Perpendicular
(9 Crashes, 0 F/SI
Crashes)
Opposite
(22 Crashes, 1 F/SI
Crashes)
Violation
Type
1 Failure to yield to
pedestrian at
intersection
(13 Crashes, 1 F/SI
Crashes)
Inappropriate turn
(9 Crashes, 1 F/SI
Crashes)
Too fast for conditions
(44 Crashes, 0 F/SI
Crashes)
2 Pedestrian failed to
yield right of way
(5 Crashes, 0 F/SI
Crashes)
Open door safely
(3 Crashes, 1 F/SI
Crashes)
Inappropriate turn
(27 Crashes, 1 F/SI
Crashes)
3 Too fast for conditions
(2 Crashes, 0 F/SI
Crashes)
Failure to yield while
entering highway
(6 Crashes, 0 F/SI
Crashes)
Failure to yield while
entering highway
(19 Crashes, 1 F/SI
Crashes)

February 5, 2024 Page 108
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Table 63: HIN Roadways in Belmont
Roadway name Length (mi)1 Motor Vehicle
HIN2
Bicyclist HIN2 Pedestrian HIN2
Alameda de las Pulgas 6.7 X X X
El Camino Real 23.5 X X X
Lyall Way 0.3 X
Old County Road 3.4 X X
Ralston Avenue 3.5 X X
[1] The length value is for the entire HIN corridor, which may represent portions in neighboring jurisdictions. [2] This column identifies whether any portion of the selected roadway is along the HIN (results may differ within select jurisdictions)
Table 64: Belmont top EPDO intersections
Intersection Name State Highway Network EPDO Score
Motor Vehicle Only
Ralston Avenue / Belmont Canyon Road N 263
Ralston Avenue / Pullman Avenue N 247
El Camino Real / Ralston Avenue Y 128
Pedestrian
Alameda De las Pulgas / Carlmont Drive N 22
El Camino Real / Belmont Avenue Y 22
El Camino Real / Ralston Avenue Y 22
Bicycle
El Camino Real / W 39th Avenue Y 191
El Camino Real / Ralston Avenue Y 55
Ralston Avenue / Academy Avenue N 22

February 5, 2024 Page 109
Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Brisbane
The following tables show crash statistics for key variables, locations along the modal HINs, and top intersections based on EPDO. The intersection table is shared for informational purposes and does not represent a potential project list, which will be evaluated and developed as part of a subsequent task. The modal HINs can be viewed dynamically online at this site. 24 Table 65: Crash Statistics for Key Variables, Brisbane Variable Rank Pedestrian Bicyclist Motorists
All Crashes 8 Crashes, 6 F/SI
Crashes
5 Crashes, 2 F/SI
Crashes
56 Crashes, 4 F/SI
Crashes
Crash
Location
1 Segment
(4 Crashes, 3 F/SI
Crashes)
Segment
(4 Crashes, 1 F/SI
Crashes)
Segment
(26 Crashes, 3 F/SI
Crashes)
2 Signalized
Intersection
(2 Crashes, 2 F/SI
Crashes)
Signalized Intersection
(1 Crashes, 1 F/SI
Crashes)
Signalized Intersection
(19 Crashes, 1 F/SI
Crashes)
3 Unsignalized
Intersection
(2 Crashes, 1 F/SI
Crashes)
Unsignalized
Intersection
(11 Crashes, 0 F/SI
Crashes)
Crash Type
1 Perpendicular - Other
(2 Crashes, 2 F/SI
Crashes)
Same Direction- Other
(2 Crashes, 1 F/SI
Crashes)
Rear End
(14 Crashes, 1 F/SI
Crashes)
2 Perpendicular - Ped
Crossing Not in
Crosswalk, MV
Proceeding Straight
(1 Crashes, 1 F/SI
Crashes)
Solo - Overturned
(1 Crashes, 1 F/SI
Crashes)
Broadside
(16 Crashes, 1 F/SI
Crashes)
3 Perpendicular - Ped
In Road, Including
Shoulder, MV
Proceeding Straight
(1 Crashes, 1 F/SI
Crashes)
Solo - Other
(1 Crashes, 0 F/SI
Crashes)
Hit Object
(12 Crashes, 1 F/SI
Crashes)
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Variable Rank Pedestrian Bicyclist Motorists
Functional
Classification
1 primary
(4 Crashes, 4 F/SI
Crashes)
primary
(2 Crashes, 1 F/SI
Crashes)
primary
(34 Crashes, 2 F/SI
Crashes)
2 tertiary
(4 Crashes, 2 F/SI
Crashes)
secondary
(1 Crashes, 1 F/SI
Crashes)
tertiary
(14 Crashes, 2 F/SI
Crashes)
3 tertiary
(2 Crashes, 0 F/SI
Crashes)
secondary
(5 Crashes, 0 F/SI
Crashes)
Lighting
Condition
1 Dark
(4 Crashes, 4 F/SI
Crashes)
Light
(5 Crashes, 2 F/SI
Crashes)
Light
(42 Crashes, 2 F/SI
Crashes)
2 Light
(4 Crashes, 2 F/SI
Crashes)
Dark
(14 Crashes, 2 F/SI
Crashes)
Posted
Speed Limit
1 <=25
(4 Crashes, 2 F/SI
Crashes)
50+
(1 Crashes, 1 F/SI
Crashes)
45 (30 Crashes, 3 F/SI
Crashes)
2 45
(2 Crashes, 2 F/SI
Crashes)
<=25
(3 Crashes, 1 F/SI
Crashes)
<=25
(21 Crashes, 1 F/SI
Crashes)
3 50+
(2 Crashes, 2 F/SI
Crashes)
45 (1 Crashes, 0 F/SI
Crashes)
50+
(5 Crashes, 0 F/SI
Crashes)
Pre-Crash
Movement
1 Other
(3 Crashes, 2 F/SI
Crashes)
Other
(3 Crashes, 1 F/SI
Crashes)
Other
(18 Crashes, 1 F/SI
Crashes)
2 Ped Crossing Not in
Crosswalk, MV
Proceeding Straight
(2 Crashes, 2 F/SI
Crashes)
Overturned
(1 Crashes, 1 F/SI
Crashes)
Hit Object
(12 Crashes, 1 F/SI
Crashes)

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Variable Rank Pedestrian Bicyclist Motorists
3 Ped In Road,
Including Shoulder,
MV Proceeding
Straight
(1 Crashes, 1 F/SI
Crashes)
Bike Not Stated, MV
Making Right Turn
(1 Crashes, 0 F/SI
Crashes)
MV Proceeding
Straight, MV
Proceeding Straight
(2 Crashes, 1 F/SI
Crashes)
Relative
Direction of
Travel
1 Perpendicular
(5 Crashes, 5 F/SI
Crashes)
Same
(3 Crashes, 1 F/SI
Crashes)
Same
(20 Crashes, 2 F/SI
Crashes)
2 Unknown
(2 Crashes, 1 F/SI
Crashes)
Solo
(2 Crashes, 1 F/SI
Crashes)
Solo
(16 Crashes, 1 F/SI
Crashes)
2 Same Direction
(1 Crashes, 0 F/SI
Crashes)
Perpendicular
(12 Crashes, 1 F/SI
Crashes)
Violation
Type
1 Pedestrian failed to
yield right of way
(3 Crashes, 3 F/SI
Crashes)
Too fast for conditions
(1 Crashes, 1 F/SI
Crashes)
Driving while under
the influence of
alcohol
(9 Crashes, 2 F/SI
Crashes)
2 Failure to yield to
pedestrian at
intersection
(3 Crashes, 2 F/SI
Crashes)
Unknown
(2 Crashes, 1 F/SI
Crashes)
Broke law while driving
under the influence of
alcohol
(1 Crashes, 1 F/SI
Crashes)
3 Too fast for conditions
(1 Crashes, 1 F/SI
Crashes)
Allow adequate
space for passing
(1 Crashes, 0 F/SI
Crashes)
Disregarded signal
(2 Crashes, 1 F/SI
Crashes)

February 5, 2024 Page 112
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Table 66: HIN Roadways in Brisbane
Roadway name Length (mi)1 Motor
Vehicle HIN2
Bicyclist HIN2 Pedestrian
HIN2
Bayshore Boulevard 2.9 X X
Guadalupe Canyon Parkway 2.5 X
Sierra Point Highway 1.4 X
[1] The length value is for the entire HIN corridor, which may represent portions in neighboring jurisdictions. [2] This column identifies whether any portion of the selected roadway is along the HIN (results may differ within select jurisdictions)
Table 67: Brisbane top EPDO intersections
Intersection Name State Highway Network EPDO Score
Motor Vehicle Only
Bayshore Boulevard / Main Street N 170
Valley Drive / N Hill Drive N 39
Bayshore Boulevard / Old County Road N 33
Pedestrian
Bayshore Boulevard / Guadalupe Canyon Parkway N 240
Bicycle
Valley Drive / N Hill Drive N 120

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East Palo Alto
The following tables show crash statistics for key variables, locations along the modal HINs, and top intersections based on EPDO. The intersection table is shared for informational purposes and does not represent a potential project list, which will be evaluated and developed as part of a subsequent task. The modal HINs can be viewed dynamically online at this site. 25 Table 68: Crash Statistics for Key Variables, East Palo Alto Variable Rank Pedestrian Bicyclist Motorists
All Crashes 56 Crashes, 13 F/SI
Crashes
75 Crashes, 10 F/SI
Crashes
327 Crashes, 25 F/SI
Crashes
Crash
Location
1 Unsignalized
Intersection
(33 Crashes, 8 F/SI
Crashes)
Signalized Intersection
(28 Crashes, 6 F/SI
Crashes)
Unsignalized
Intersection
(171 Crashes, 15 F/SI
Crashes)
2 Segment
(14 Crashes, 4 F/SI
Crashes)
Unsignalized
Intersection
(34 Crashes, 3 F/SI
Crashes)
Signalized Intersection
(96 Crashes, 5 F/SI
Crashes)
3 Signalized
Intersection
(9 Crashes, 1 F/SI
Crashes)
Segment
(13 Crashes, 1 F/SI
Crashes)
Segment
(60 Crashes, 5 F/SI
Crashes)
Crash Type
1 Perpendicular - Other
(6 Crashes, 2 F/SI
Crashes)
Perpendicular - Bike
Proceeding Straight,
MV Proceeding
Straight
(13 Crashes, 1 F/SI
Crashes)
Broadside
(138 Crashes, 8 F/SI
Crashes)
2 Unknown - Other
(3 Crashes, 2 F/SI
Crashes)
Same Direction- Bike
Proceeding Straight,
MV Proceeding
Straight
(5 Crashes, 1 F/SI
Crashes)
Rear End
(75 Crashes, 3 F/SI
Crashes)
3 Unknown - Ped Not in
Road, MV
Proceeding Straight
(2 Crashes, 2 F/SI
Crashes)
Same Direction- Bike
Not Stated, MV
Proceeding Straight
(2 Crashes, 1 F/SI
Crashes)
Head-On
(32 Crashes, 4 F/SI
Crashes)
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Variable Rank Pedestrian Bicyclist Motorists
Functional
Classification
1 tertiary
(14 Crashes, 5 F/SI
Crashes)
primary
(26 Crashes, 6 F/SI
Crashes)
primary
(112 Crashes, 7 F/SI
Crashes)
2 residential
(14 Crashes, 3 F/SI
Crashes)
tertiary
(10 Crashes, 3 F/SI
Crashes)
residential
(91 Crashes, 6 F/SI
Crashes)
3 secondary
(8 Crashes, 2 F/SI
Crashes)
secondary
(19 Crashes, 1 F/SI
Crashes)
secondary
(46 Crashes, 7 F/SI
Crashes)
Lighting
Condition
1 Dark
(27 Crashes, 8 F/SI
Crashes)
Light
(55 Crashes, 4 F/SI
Crashes)
Light
(209 Crashes, 15 F/SI
Crashes)
2 Light
(29 Crashes, 5 F/SI
Crashes)
Dark
(19 Crashes, 6 F/SI
Crashes)
Dark
(117 Crashes, 10 F/SI
Crashes)
3 Unknown
(1 Crashes, 0 F/SI
Crashes)
Unknown
(1 Crashes, 0 F/SI
Crashes)
Posted
Speed Limit
1 <=25
(28 Crashes, 8 F/SI
Crashes)
35 (35 Crashes, 7 F/SI
Crashes)
<=25
(178 Crashes, 17 F/SI
Crashes)
2 35
(22 Crashes, 4 F/SI
Crashes)
<=25
(33 Crashes, 3 F/SI
Crashes)
35 (123 Crashes, 8 F/SI
Crashes)
3 Unknown
(6 Crashes, 1 F/SI
Crashes)
40 (4 Crashes, 0 F/SI
Crashes)
Unknown
(15 Crashes, 0 F/SI
Crashes)
Pre-Crash
Movement
1 Other
(13 Crashes, 4 F/SI
Crashes)
Bike Proceeding
Straight, MV
Proceeding Straight
(21 Crashes, 2 F/SI
Crashes)
Other
(92 Crashes, 6 F/SI
Crashes)

February 5, 2024 Page 115
Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Variable Rank Pedestrian Bicyclist Motorists
2 Ped Crossing Not in
Crosswalk, MV
Proceeding Straight
(12 Crashes, 3 F/SI
Crashes)
Other
(21 Crashes, 2 F/SI
Crashes)
Hit Object
(21 Crashes, 4 F/SI
Crashes)
3 Ped Not in Road, MV
Proceeding Straight
(2 Crashes, 2 F/SI
Crashes)
Bike Proceeding
Straight, MV unknown
(4 Crashes, 2 F/SI
Crashes)
MV Proceeding
Straight, MV
Proceeding Straight
(56 Crashes, 2 F/SI
Crashes)
Relative
Direction of
Travel
1 Perpendicular
(33 Crashes, 7 F/SI
Crashes)
Perpendicular
(36 Crashes, 2 F/SI
Crashes)
Perpendicular
(109 Crashes, 8 F/SI
Crashes)
2 Unknown
(12 Crashes, 5 F/SI
Crashes)
Opposite
(14 Crashes, 4 F/SI
Crashes)
Same
(89 Crashes, 4 F/SI
Crashes)
3 Same
(7 Crashes, 1 F/SI
Crashes)
Same
(15 Crashes, 3 F/SI
Crashes)
Solo
(33 Crashes, 5 F/SI
Crashes)
Violation
Type
1 Pedestrian failed to
yield right of way
(15 Crashes, 4 F/SI
Crashes)
Disregarded signal
(12 Crashes, 3 F/SI
Crashes)
Too fast for conditions
(71 Crashes, 8 F/SI
Crashes)
2 Failure to yield to
pedestrian at
intersection
(16 Crashes, 3 F/SI
Crashes)
Inappropriate turn
(15 Crashes, 1 F/SI
Crashes)
Inappropriate turn
(74 Crashes, 4 F/SI
Crashes)
3 Inappropriate turn
(6 Crashes, 2 F/SI
Crashes)
Drive on right except
when passing
(3 Crashes, 1 F/SI
Crashes)
Unknown
(17 Crashes, 3 F/SI
Crashes)

February 5, 2024 Page 116
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Table 69: HIN Roadways in East Palo Alto
Roadway name Length (mi)1 Motor Vehicle
HIN2
Bicyclist HIN2 Pedestrian HIN2
Bay Road 3.3 X X
Bayshore Rd 1.7 X X X
Capitol Avenue 0.2 X
Clarke Avenue 0.3 X
Donohoe Street 0.5 X X
Newbridge Street 1.0 X X
O’Connor Street 0.6 X
Pulgas Avenue 1.5 X X
Runnymede Street 0.9 X
Scofield Street 0.1 X
US 101 1.2 X
University Avenue 2.1 X X
Willow Road 2.3 X X
[1] The length value is for the entire HIN corridor, which may represent portions in neighboring jurisdictions. [2] This column identifies whether any portion of the selected roadway is along the HIN (results may differ within select jurisdictions)
Table 70: East Palo Alto top EPDO intersections
Intersection Name State Highway Network EPDO Score
Motor Vehicle Only
University Avenue / Weeks Street Y 404
Donohoe Street / University Avenue Y 327
University Avenue / Bay Road Y 312
Pedestrian

February 5, 2024 Page 117
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Intersection Name State Highway Network EPDO Score
University Avenue / Weeks Street Y 197
Bay Road / Illinois Street / Clarke Avenue N 191
University Avenue / Bell Street Y 137
Bicycle
Donohoe Street / University Avenue N 279
University Avenue / Adams Drive Y 191
Willow Road / Newbridge Street Y 164

February 5, 2024 Page 118
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Foster City
The following tables show crash statistics for key variables, locations along the modal HINs, and top intersections based on EPDO. The intersection table is shared for informational purposes and does not represent a potential project list, which will be evaluated and developed as part of a subsequent task. The modal HINs can be viewed dynamically online at this site. 26 Table 71: Crash Statistics for Key Variables, Foster City Variable Rank Pedestrian Bicyclist Motorists
All Crashes 20 Crashes, 6 F/SI
Crashes
16 Crashes, 3 F/SI
Crashes
114 Crashes, 4 F/SI
Crashes
Crash
Location
1 Signalized
Intersection
(9 Crashes, 4 F/SI
Crashes)
Segment
(6 Crashes, 2 F/SI
Crashes)
Segment
(40 Crashes, 2 F/SI
Crashes)
2 Unsignalized
Intersection
(8 Crashes, 1 F/SI
Crashes)
Signalized Intersection
(5 Crashes, 1 F/SI
Crashes)
Signalized Intersection
(42 Crashes, 2 F/SI
Crashes)
3 Segment
(3 Crashes, 1 F/SI
Crashes)
Unsignalized
Intersection
(5 Crashes, 0 F/SI
Crashes)
Unsignalized
Intersection
(32 Crashes, 0 F/SI
Crashes)
Crash Type
1 Same Direction- Ped
Crossing in Crosswalk
Not at Intersection,
MV Proceeding
Straight
(1 Crashes, 0 F/SI
Crashes)
Same Direction- Other
(2 Crashes, 1 F/SI
Crashes)
Hit Object
(16 Crashes, 2 F/SI
Crashes)
2 Unknown - Ped Not in
Road, MV
Proceeding Straight
(1 Crashes, 0 F/SI
Crashes)
Perpendicular - Bike
Not Stated, MV
Proceeding Straight
(1 Crashes, 1 F/SI
Crashes)
Rear End
(44 Crashes, 0 F/SI
Crashes)
3 Perpendicular - Ped
Crossing in Crosswalk
at Intersection, MV
Proceeding Straight
(6 Crashes, 2 F/SI
Crashes)
Perpendicular - Bike
Proceeding Straight,
MV Proceeding
Straight
(1 Crashes, 1 F/SI
Crashes)
Sideswipe
(15 Crashes, 1 F/SI
Crashes)
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Variable Rank Pedestrian Bicyclist Motorists
Functional
Classification
1 secondary
(16 Crashes, 5 F/SI
Crashes)
secondary
(12 Crashes, 2 F/SI
Crashes)
secondary
(68 Crashes, 1 F/SI
Crashes)
2 primary
(1 Crashes, 1 F/SI
Crashes)
residential
(2 Crashes, 1 F/SI
Crashes)
ramp
(5 Crashes, 2 F/SI
Crashes)
3 tertiary
(2 Crashes, 0 F/SI
Crashes)
primary
(2 Crashes, 0 F/SI
Crashes)
primary
(19 Crashes, 1 F/SI
Crashes)
Lighting
Condition
1 Dark
(8 Crashes, 4 F/SI
Crashes)
Light
(15 Crashes, 3 F/SI
Crashes)
Light
(86 Crashes, 1 F/SI
Crashes)
2 Light
(12 Crashes, 2 F/SI
Crashes)
Dark
(1 Crashes, 0 F/SI
Crashes)
Dark
(28 Crashes, 3 F/SI
Crashes)
Posted
Speed Limit
1 35
(19 Crashes, 6 F/SI
Crashes)
35 (13 Crashes, 2 F/SI
Crashes)
35 (91 Crashes, 2 F/SI
Crashes)
2 <=25
(1 Crashes, 0 F/SI
Crashes)
<=25
(2 Crashes, 1 F/SI
Crashes)
50+
(9 Crashes, 2 F/SI
Crashes)
3 50+
(1 Crashes, 0 F/SI
Crashes)
<=25
(12 Crashes, 0 F/SI
Crashes)
Pre-Crash
Movement
1 Ped Crossing in
Crosswalk Not at
Intersection, MV
Proceeding Straight
(1 Crashes, 0 F/SI
Crashes)
Other
(3 Crashes, 1 F/SI
Crashes)
Hit Object
(14 Crashes, 2 F/SI
Crashes)
2 Ped Not in Road, MV
Proceeding Straight
(1 Crashes, 0 F/SI
Crashes)
Bike Not Stated, MV
Proceeding Straight
(1 Crashes, 1 F/SI
Crashes)
Other
(26 Crashes, 1 F/SI
Crashes)

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Variable Rank Pedestrian Bicyclist Motorists
3 Ped Crossing in
Crosswalk at
Intersection, MV
Proceeding Straight
(6 Crashes, 2 F/SI
Crashes)
Bike Proceeding
Straight, MV
Proceeding Straight
(2 Crashes, 1 F/SI
Crashes)
MV Making Left Turn,
MV Proceeding
Straight
(11 Crashes, 1 F/SI
Crashes)
Relative
Direction of
Travel
1 Unknown
(1 Crashes, 0 F/SI
Crashes)
Perpendicular
(6 Crashes, 2 F/SI
Crashes)
Same
(59 Crashes, 1 F/SI
Crashes)
2 Perpendicular
(15 Crashes, 6 F/SI
Crashes)
Same
(5 Crashes, 1 F/SI
Crashes)
Solo
(19 Crashes, 2 F/SI
Crashes)
3 Opposite
(2 Crashes, 0 F/SI
Crashes)
Opposite
(3 Crashes, 0 F/SI
Crashes)
Perpendicular
(23 Crashes, 1 F/SI
Crashes)
Violation
Type
1 Failure to yield to
pedestrian at
intersection
(11 Crashes, 2 F/SI
Crashes)
Failure to yield while
entering highway
(2 Crashes, 1 F/SI
Crashes)
Too fast for conditions
(44 Crashes, 1 F/SI
Crashes)
2 Unknown
(1 Crashes, 1 F/SI
Crashes)
Failed to drive within
one lane
(1 Crashes, 1 F/SI
Crashes)
Disregarded signal
(15 Crashes, 2 F/SI
Crashes)
3 Peds yield to drivers
already in an
intersection
(1 Crashes, 1 F/SI
Crashes)
Disregarded signal
(2 Crashes, 1 F/SI
Crashes)
Driving while under
the influence of
alcohol
(5 Crashes, 1 F/SI
Crashes)

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Table 72: HIN Roadways in Foster City
Roadway name Length (mi)1 Motor Vehicle
HIN2
Bicyclist HIN2 Pedestrian HIN2
Foster City Boulevard 1.7 X X
Hillsdale Boulevard 3.9 X X X
Metro Center Boulevard 0.7 X
Shell Boulevard 1.1 X
[1] The length value is for the entire HIN corridor, which may represent portions in neighboring jurisdictions. [2] This column identifies whether any portion of the selected roadway is along the HIN (results may differ within select jurisdictions)
Table 73: Foster City top EPDO intersections
Intersection Name State Highway
Network
EPDO
Score
Motor Vehicle Only
Foster City Boulevard / Triton Drive / Metro Center
Boulevard
N 188
Edgewater Boulevard / E Hillsdale Boulevard N 137
E Hillside Boulevard / Shell Boulevard N 111
Pedestrian
Shell Boulevard / Catamaran Street N 202
Foster City Boulevard / E Hillsdale Boulevard N 131
Shell Boulevard / Foster Square Lane N 120
Bicycle
Foster City Boulevard / E Hillsdale Boulevard N 120
E Hillside Boulevard / Shell Boulevard N 22
Edgewater Boulevard / SR 92 Ramp / Emerald Bay Lane N 11

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Half Moon Bay
The following tables show crash statistics for key variables, locations along the modal HINs, and top intersections based on EPDO. The intersection table is shared for informational purposes and does not represent a potential project list, which will be evaluated and developed as part of a subsequent task. The modal HINs can be viewed dynamically online at this site. 27 Table 74: Crash Statistics for Key Variables, Half Moon Bay Variable Rank Pedestrian Bicyclist Motorists
All Crashes 13 Crashes, 3 F/SI
Crashes
15 Crashes, 2 F/SI
Crashes
117 Crashes, 12 F/SI
Crashes
Crash
Location
1 Segment
(7 Crashes, 2 F/SI
Crashes)
Unsignalized
Intersection
(5 Crashes, 1 F/SI
Crashes)
Segment
(66 Crashes, 8 F/SI
Crashes)
2 Unsignalized
Intersection
(6 Crashes, 1 F/SI
Crashes)
Signalized Intersection
(2 Crashes, 1 F/SI
Crashes)
Unsignalized
Intersection
(32 Crashes, 4 F/SI
Crashes)
3 Segment
(8 Crashes, 0 F/SI
Crashes)
Signalized Intersection
(19 Crashes, 0 F/SI
Crashes)
Crash Type
1 Same Direction -
Other
(1 Crashes, 1 F/SI
Crashes)
Same Direction -
Other
(2 Crashes, 1 F/SI
Crashes)
Rear End
(50 Crashes, 3 F/SI
Crashes)
2 Perpendicular - Ped
Crossing Not in
Crosswalk, MV
Proceeding Straight
(2 Crashes, 1 F/SI
Crashes)
Perpendicular - Bike
Proceeding Straight,
MV Making Left Turn
(1 Crashes, 1 F/SI
Crashes)
Broadside
(22 Crashes, 4 F/SI
Crashes)
3 Unknown - Other
(2 Crashes, 1 F/SI
Crashes)
Same Direction - Bike
Making Left Turn, MV
Proceeding Straight
(3 Crashes, 0 F/SI
Crashes)
Hit Object
(14 Crashes, 2 F/SI
Crashes)
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Variable Rank Pedestrian Bicyclist Motorists
Functional
Classification
1 residential
(3 Crashes, 1 F/SI
Crashes)
trunk
(8 Crashes, 2 F/SI
Crashes)
trunk
(75 Crashes, 8 F/SI
Crashes)
2 trunk
(2 Crashes, 1 F/SI
Crashes)
tertiary
(3 Crashes, 0 F/SI
Crashes)
primary
(12 Crashes, 2 F/SI
Crashes)
3 secondary
(2 Crashes, 1 F/SI
Crashes)
primary
(2 Crashes, 0 F/SI
Crashes)
Unknown
(16 Crashes, 1 F/SI
Crashes)
Lighting
Condition
1 Dark
(6 Crashes, 2 F/SI
Crashes)
Light
(12 Crashes, 2 F/SI
Crashes)
Light
(81 Crashes, 10 F/SI
Crashes)
2 Light
(7 Crashes, 1 F/SI
Crashes)
Dark
(3 Crashes, 0 F/SI
Crashes)
Dark
(36 Crashes, 2 F/SI
Crashes)
Posted
Speed Limit
1 <=25
(7 Crashes, 1 F/SI
Crashes)
40 (8 Crashes, 2 F/SI
Crashes)
40 (71 Crashes, 7 F/SI
Crashes)
2 35
(3 Crashes, 1 F/SI
Crashes)
35 (4 Crashes, 0 F/SI
Crashes)
35 (14 Crashes, 3 F/SI
Crashes)
3 40
(2 Crashes, 1 F/SI
Crashes)
<=25
(3 Crashes, 0 F/SI
Crashes)
Unknown
(16 Crashes, 1 F/SI
Crashes)
Pre-Crash
Movement
1 Other
(6 Crashes, 2 F/SI
Crashes)
Other
(3 Crashes, 1 F/SI
Crashes)
Other
(33 Crashes, 4 F/SI
Crashes)
2 Ped Crossing Not in
Crosswalk, MV
Proceeding Straight
(2 Crashes, 1 F/SI
Crashes)
Bike Proceeding
Straight, MV Making
Left Turn
(1 Crashes, 1 F/SI
Crashes)
Hit Object
(14 Crashes, 2 F/SI
Crashes)

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Variable Rank Pedestrian Bicyclist Motorists
3 Ped Crossing in
Crosswalk at
Intersection, MV
Making Left Turn
(2 Crashes, 0 F/SI
Crashes)
Bike Proceeding
Straight, MV Making
Right Turn
(4 Crashes, 0 F/SI
Crashes)
MV Proceeding
Straight, MV Stopped
(17 Crashes, 1 F/SI
Crashes)
Relative
Direction of
Travel
1 Perpendicular
(8 Crashes, 1 F/SI
Crashes)
Same
(8 Crashes, 1 F/SI
Crashes)
Same
(62 Crashes, 4 F/SI
Crashes)
2 Same
(1 Crashes, 1 F/SI
Crashes)
Perpendicular
(7 Crashes, 1 F/SI
Crashes)
Perpendicular
(15 Crashes, 3 F/SI
Crashes)
3 Unknown
(2 Crashes, 1 F/SI
Crashes)
Opposite
(13 Crashes, 3 F/SI
Crashes)
Violation
Type
1 Inappropriate turn
(2 Crashes, 1 F/SI
Crashes)
Open door safely
(1 Crashes, 0 F/SI
Crashes)
Too fast for conditions
(43 Crashes, 2 F/SI
Crashes)
2 Unknown
(4 Crashes, 1 F/SI
Crashes)
Unknown
(2 Crashes, 2 F/SI
Crashes)
Driving while under
the influence of
alcohol
(14 Crashes, 3 F/SI
Crashes)
3 Illegal Segment
crossing
(1 Crashes, 1 F/SI
Crashes)
Inappropriate turn
(5 Crashes, 0 F/SI
Crashes)
Allow adequate
space for passing
(10 Crashes, 2 F/SI
Crashes)

February 5, 2024 Page 125
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Table 75: HIN Roadways in Half Moon Bay
Roadway name Length (mi)1 Motor Vehicle
HIN2
Bicyclist HIN2 Pedestrian HIN2
Main Street 2.0 X
SR 1 30.4 X X
San Mateo Road 6.6 X
[1] The length value is for the entire HIN corridor, which may represent portions in neighboring jurisdictions. [2] This column identifies whether any portion of the selected roadway is along the HIN (results may differ within select jurisdictions)
Table 76: Half Moon Bay top EPDO intersections
Intersection Name State Highway Network EPDO Score
Motor Vehicle Only
Cabrillo Highway N / Purisima Way Y 202
Cabrillo Highway N / Guerrero Avenue Y 202
Cabrillo Highway S / Wavecrest Road Y 202
Pedestrian
Main Street / Lewis Foster Drive N 11
Bicycle
Cabrillo Highway N / Roosevelt Boulevard Y 191
Cabrillo Highway S / Kelly Avenue Y 120
San Mateo Road / Main Street Y 11

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Hillsborough
The following tables show crash statistics for key variables, locations along the modal HINs, and top intersections based on EPDO. The intersection table is shared for informational purposes and does not represent a potential project list, which will be evaluated and developed as part of a subsequent task. The modal HINs can be viewed dynamically online at this site. 28 Table 77: Crash Statistics for Key Variables, Hillsborough Variable Rank Pedestrian Bicyclist Motorists
All Crashes 4 Crashes, 1 F/SI
Crashes
7 Crashes, 2 F/SI
Crashes
27 Crashes, 1 F/SI
Crashes
Crash
Location
1 Unsignalized
Intersection
(1 Crashes, 1 F/SI
Crashes)
Unsignalized
Intersection
(5 Crashes, 2 F/SI
Crashes)
Segment
(12 Crashes, 1 F/SI
Crashes)
2 Segment
(3 Crashes, 0 F/SI
Crashes)
Segment
(2 Crashes, 0 F/SI
Crashes)
Unsignalized
Intersection
(15 Crashes, 0 F/SI
Crashes)
Crash Type
1 Perpendicular - Ped
In Road, Including
Shoulder, MV
Proceeding Straight
(1 Crashes, 1 F/SI
Crashes)
Perpendicular - Other
(2 Crashes, 1 F/SI
Crashes)
Broadside
(6 Crashes, 1 F/SI
Crashes)
2 Perpendicular - Other
(1 Crashes, 0 F/SI
Crashes)
Same Direction -
Other
(1 Crashes, 1 F/SI
Crashes)
Hit Object
(8 Crashes, 0 F/SI
Crashes)
3 Same Direction -
Other
(1 Crashes, 0 F/SI
Crashes)
Opposite - Bike
Proceeding Straight,
MV Making Left Turn
(1 Crashes, 0 F/SI
Crashes)
Head-On
(6 Crashes, 0 F/SI
Crashes)
Functional
Classification
1 residential
(4 Crashes, 1 F/SI
Crashes)
residential
(3 Crashes, 1 F/SI
Crashes)
secondary
(6 Crashes, 1 F/SI
Crashes)
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Variable Rank Pedestrian Bicyclist Motorists
2 tertiary
(2 Crashes, 1 F/SI
Crashes)
residential
(17 Crashes, 0 F/SI
Crashes)
3 secondary
(2 Crashes, 0 F/SI
Crashes)
tertiary
(4 Crashes, 0 F/SI
Crashes)
Lighting
Condition
1 Light
(4 Crashes, 1 F/SI
Crashes)
Light
(7 Crashes, 2 F/SI
Crashes)
Light
(18 Crashes, 1 F/SI
Crashes)
2 Dark
(9 Crashes, 0 F/SI
Crashes)
Posted
Speed Limit
1 <=25
(4 Crashes, 1 F/SI
Crashes)
<=25
(5 Crashes, 2 F/SI
Crashes)
30 (4 Crashes, 1 F/SI
Crashes)
2 45
(2 Crashes, 0 F/SI
Crashes)
<=25
(20 Crashes, 0 F/SI
Crashes)
3 45
(3 Crashes, 0 F/SI
Crashes)
Pre-Crash
Movement
1 Ped In Road,
Including Shoulder,
MV Proceeding
Straight
(2 Crashes, 1 F/SI
Crashes)
Other
(4 Crashes, 2 F/SI
Crashes)
MV Proceeding
Straight, MV unknown
(1 Crashes, 0 F/SI
Crashes)
2 Other
(2 Crashes, 0 F/SI
Crashes)
Bike Proceeding
Straight, MV Making
Left Turn
(1 Crashes, 0 F/SI
Crashes)
Other
(7 Crashes, 1 F/SI
Crashes)
3 Bike Proceeding
Straight, MV unknown
(1 Crashes, 0 F/SI
Crashes)
Hit Object
(8 Crashes, 0 F/SI
Crashes)

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Variable Rank Pedestrian Bicyclist Motorists
Relative
Direction of
Travel
1 Perpendicular
(2 Crashes, 1 F/SI
Crashes)
Perpendicular
(4 Crashes, 1 F/SI
Crashes)
Opposite
(8 Crashes, 1 F/SI
Crashes)
2 Same
(2 Crashes, 0 F/SI
Crashes)
Same
(1 Crashes, 1 F/SI
Crashes)
Solo
(10 Crashes, 0 F/SI
Crashes)
3 Opposite
(2 Crashes, 0 F/SI
Crashes)
Same
(4 Crashes, 0 F/SI
Crashes)
Violation
Type
1 Unknown
(1 Crashes, 1 F/SI
Crashes)
Drivers/cyclists yield to
drivers already in an
intersection
(4 Crashes, 2 F/SI
Crashes)
Inappropriate turn
(7 Crashes, 1 F/SI
Crashes)
2 Too fast for conditions
(1 Crashes, 0 F/SI
Crashes)
Failure to yield while
entering highway
(2 Crashes, 0 F/SI
Crashes)
Too fast for conditions
(3 Crashes, 0 F/SI
Crashes)
3 Failure to yield to
pedestrian at
intersection
(1 Crashes, 0 F/SI
Crashes)
Inappropriate turn
(1 Crashes, 0 F/SI
Crashes)
Drivers/cyclists yield to
drivers already in an
intersection
(2 Crashes, 0 F/SI
Crashes)

February 5, 2024 Page 129
Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Table 78: HIN Roadways in Hillsborough
Roadway name Length (mi)1 Motor Vehicle
HIN2
Bicyclist HIN2 Pedestrian HIN2
El Camino Real 23.5 X X X
Golf Course Drive 0.2 X
[1] The length value is for the entire HIN corridor, which may represent portions in neighboring jurisdictions. [2] This column identifies whether any portion of the selected roadway is along the HIN (results may differ within select jurisdictions)
Table 79: Hillsborough top EPDO intersections
Intersection Name State Highway
Network
EPDO
Score
Motor Vehicle Only
Skyline Boulevard / Skyfarm Drive Y 11
Crystal Springs Road / Merner Road N 11
Skyline Boulevard / Chateau Drive Y 11
Pedestrian1
Bicycle
Hayne Road / Golf Course Drive / Black Mountain Road /
SR 35 Ramp Y 11
[1] No pedestrian crashes have been reported in Hillsborough during the data analysis period (2018-2022)

February 5, 2024 Page 130
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Portola Valley
The following tables show crash statistics for key variables, locations along the modal HINs, and top intersections based on EPDO. The intersection table is shared for informational purposes and does not represent a potential project list, which will be evaluated and developed as part of a subsequent task. The modal HINs can be viewed dynamically online at this site. 29 Table 80: Crash Statistics for Key Variables, Portola Valley Variable Rank Pedestrian Bicyclist Motorists
All Crashes 3 Crashes, 0 F/SI
Crashes
25 Crashes, 12 F/SI
Crashes
11 Crashes, 2 F/SI
Crashes
Crash
Location
1 Unsignalized
Intersection
(1 Crashes, 0 F/SI
Crashes)
Unsignalized
Intersection
(14 Crashes, 6 F/SI
Crashes)
Unsignalized
Intersection
(7 Crashes, 2 F/SI
Crashes)
2 Segment
(2 Crashes, 0 F/SI
Crashes)
Segment
(11 Crashes, 6 F/SI
Crashes)
Segment
(4 Crashes, 0 F/SI
Crashes)
Crash Type
1 Unknown - Bike
Proceeding Straight,
Ped In Road,
Including Shoulder
(1 Crashes, 0 F/SI
Crashes)
Solo - Other
(7 Crashes, 6 F/SI
Crashes)
Hit Object
(6 Crashes, 2 F/SI
Crashes)
2 Same Direction -
Other
(1 Crashes, 0 F/SI
Crashes)
Solo - Overturned
(2 Crashes, 2 F/SI
Crashes)
Broadside
(2 Crashes, 0 F/SI
Crashes)
3 Same Direction - Bike
Not Stated, Ped In
Road, Including
Shoulder
(1 Crashes, 0 F/SI
Crashes)
Perpendicular - Other
(2 Crashes, 1 F/SI
Crashes)
Head-On
(1 Crashes, 0 F/SI
Crashes)
Functional
Classification
1 residential
(1 Crashes, 0 F/SI
Crashes)
secondary
(18 Crashes, 9 F/SI
Crashes)
residential
(3 Crashes, 1 F/SI
Crashes)
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Variable Rank Pedestrian Bicyclist Motorists
2 secondary
(2 Crashes, 0 F/SI
Crashes)
residential
(3 Crashes, 2 F/SI
Crashes)
tertiary
(1 Crashes, 1 F/SI
Crashes)
3 tertiary
(2 Crashes, 1 F/SI
Crashes)
secondary
(6 Crashes, 0 F/SI
Crashes)
Lighting
Condition
1 Light
(3 Crashes, 0 F/SI
Crashes)
Light
(25 Crashes, 12 F/SI
Crashes)
Dark
(5 Crashes, 2 F/SI
Crashes)
2 Light
(6 Crashes, 0 F/SI
Crashes)
Posted
Speed Limit
1 <=25
(1 Crashes, 0 F/SI
Crashes)
35 (19 Crashes, 10 F/SI
Crashes)
35 (9 Crashes, 2 F/SI
Crashes)
2 35
(2 Crashes, 0 F/SI
Crashes)
<=25
(2 Crashes, 1 F/SI
Crashes)
<=25
(1 Crashes, 0 F/SI
Crashes)
3 45
(2 Crashes, 1 F/SI
Crashes)
(1 Crashes, 0 F/SI
Crashes)
Pre-Crash
Movement
1 Bike Proceeding
Straight, Ped In Road,
Including Shoulder
(1 Crashes, 0 F/SI
Crashes)
Other
(11 Crashes, 8 F/SI
Crashes)
Hit Object
(5 Crashes, 2 F/SI
Crashes)
2 Other
(1 Crashes, 0 F/SI
Crashes)
Overturned
(2 Crashes, 2 F/SI
Crashes)
Other
(4 Crashes, 0 F/SI
Crashes)
3 Bike Not Stated, Ped
In Road, Including
Shoulder
(1 Crashes, 0 F/SI
Crashes)
Bike Not Stated, MV
Making Left Turn
(4 Crashes, 1 F/SI
Crashes)
Head-On
(1 Crashes, 0 F/SI
Crashes)

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Variable Rank Pedestrian Bicyclist Motorists
Relative
Direction of
Travel
1 Unknown
(1 Crashes, 0 F/SI
Crashes)
Solo
(10 Crashes, 8 F/SI
Crashes)
Solo
(7 Crashes, 2 F/SI
Crashes)
2 Same
(2 Crashes, 0 F/SI
Crashes)
Perpendicular
(7 Crashes, 3 F/SI
Crashes)
Perpendicular
(2 Crashes, 0 F/SI
Crashes)
3 Same
(5 Crashes, 1 F/SI
Crashes)
Unknown
(1 Crashes, 0 F/SI
Crashes)
Violation
Type
1 Too fast for conditions
(1 Crashes, 0 F/SI
Crashes)
Too fast for conditions
(11 Crashes, 6 F/SI
Crashes)
Inappropriate turn
(4 Crashes, 1 F/SI
Crashes)
2 Inappropriate turn
(1 Crashes, 0 F/SI
Crashes)
Unknown
(4 Crashes, 3 F/SI
Crashes)
Broke law while driving
under the influence of
alcohol
(1 Crashes, 1 F/SI
Crashes)
3 Unknown
(1 Crashes, 0 F/SI
Crashes)
Failure to yield while
making a turn
(5 Crashes, 1 F/SI
Crashes)
Too fast for conditions
(4 Crashes, 0 F/SI
Crashes)

February 5, 2024 Page 133
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Table 81: HIN Roadways in Portola Valley
Roadway name Length (mi)1 Motor Vehicle
HIN2
Bicyclist HIN2 Pedestrian HIN2
Alpine Road 7.5 X X
Portola Road 4.2 X X
[1] The length value is for the entire HIN corridor, which may represent portions in neighboring jurisdictions. [2] This column identifies whether any portion of the selected roadway is along the HIN (results may differ within select jurisdictions)
Table 82: Portola Valley top EPDO intersections
Intersection Name State Highway Network EPDO Score
Motor Vehicle Only
Alpine Road / Portola Road N 22
Alpine Road / Indian Xing N 11
Alpine Road / Golden Oak Drive N 6
Pedestrian1
Bicycle
Alpine Road / Arastradero Road N 213
Alpine Road / Los Trancos Road N 202
Portola Road / Stonegate Road N 191
[1] No pedestrian crashes have occurred in Portola Valley during the data analysis period (2018-2022.

February 5, 2024 Page 134
Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

San Carlos
The following tables show crash statistics for key variables, locations along the modal HINs, and top intersections based on EPDO. The intersection table is shared for informational purposes and does not represent a potential project list, which will be evaluated and developed as part of a subsequent task. The modal HINs can be viewed dynamically online at this site. 30 Table 83: Crash Statistics for Key Variables, San Carlos Variable Rank Pedestrian Bicyclist Motorists
All Crashes 38 Crashes, 9 F/SI
Crashes
40 Crashes, 4 F/SI
Crashes
203 Crashes, 18 F/SI
Crashes
Crash
Location
1 Unsignalized
Intersection
(18 Crashes, 3 F/SI
Crashes)
Unsignalized
Intersection
(12 Crashes, 2 F/SI
Crashes)
Segment
(73 Crashes, 6 F/SI
Crashes)
2 Signalized
Intersection
(11 Crashes, 4 F/SI
Crashes)
Segment
(21 Crashes, 1 F/SI
Crashes)
Signalized Intersection
(71 Crashes, 7 F/SI
Crashes)
3 Segment
(9 Crashes, 2 F/SI
Crashes)
Signalized Intersection
(7 Crashes, 1 F/SI
Crashes)
Unsignalized
Intersection
(59 Crashes, 5 F/SI
Crashes)
Crash Type
1 Perpendicular - Ped
Crossing in Crosswalk
at Intersection, MV
Making Left Turn
(7 Crashes, 3 F/SI
Crashes)
Perpendicular - Bike
Proceeding Straight,
MV Making Right Turn
(3 Crashes, 1 F/SI
Crashes)
Broadside
(73 Crashes, 8 F/SI
Crashes)
2 Perpendicular - Ped
Crossing Not in
Crosswalk, MV
Proceeding Straight
(3 Crashes, 2 F/SI
Crashes)
Perpendicular - Bike
Not Stated, MV
Making Left Turn
(1 Crashes, 1 F/SI
Crashes)
Rear End
(58 Crashes, 3 F/SI
Crashes)
3 Perpendicular - Ped
Crossing in Crosswalk
at Intersection, MV
Proceeding Straight
(5 Crashes, 2 F/SI
Crashes)
Perpendicular - Other
(3 Crashes, 1 F/SI
Crashes)
Sideswipe
(21 Crashes, 3 F/SI
Crashes)
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Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Variable Rank Pedestrian Bicyclist Motorists
Functional
Classification
1 secondary
(17 Crashes, 7 F/SI
Crashes)
secondary
(24 Crashes, 3 F/SI
Crashes)
secondary
(102 Crashes, 12 F/SI
Crashes)
2 residential
(9 Crashes, 1 F/SI
Crashes)
primary
(6 Crashes, 1 F/SI
Crashes)
primary
(41 Crashes, 3 F/SI
Crashes)
3 primary
(7 Crashes, 1 F/SI
Crashes)
residential
(4 Crashes, 0 F/SI
Crashes)
residential
(31 Crashes, 2 F/SI
Crashes)
Lighting
Condition
1 Light
(28 Crashes, 8 F/SI
Crashes)
Light
(36 Crashes, 3 F/SI
Crashes)
Light
(160 Crashes, 11 F/SI
Crashes)
2 Dark
(10 Crashes, 1 F/SI
Crashes)
Dark
(4 Crashes, 1 F/SI
Crashes)
Dark
(43 Crashes, 7 F/SI
Crashes)
Posted
Speed Limit
1 35
(11 Crashes, 4 F/SI
Crashes)
45 (19 Crashes, 3 F/SI
Crashes)
45 (86 Crashes, 10 F/SI
Crashes)
2 45
(12 Crashes, 3 F/SI
Crashes)
30 (4 Crashes, 1 F/SI
Crashes)
<=25
(41 Crashes, 4 F/SI
Crashes)
3 <=25
(14 Crashes, 2 F/SI
Crashes)
<=25
(8 Crashes, 0 F/SI
Crashes)
35 (40 Crashes, 3 F/SI
Crashes)
Pre-Crash
Movement
1 Ped Crossing in
Crosswalk at
Intersection, MV
Making Left Turn
(11 Crashes, 3 F/SI
Crashes)
Other
(14 Crashes, 1 F/SI
Crashes)
MV Proceeding
Straight, MV
Proceeding Straight
(46 Crashes, 4 F/SI
Crashes)

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Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Variable Rank Pedestrian Bicyclist Motorists
2 Ped Crossing in
Crosswalk at
Intersection, MV
Proceeding Straight
(6 Crashes, 2 F/SI
Crashes)
Bike Proceeding
Straight, MV Making
Right Turn
(8 Crashes, 1 F/SI
Crashes)
Other
(39 Crashes, 3 F/SI
Crashes)
3 Ped Crossing Not in
Crosswalk, MV
Proceeding Straight
(3 Crashes, 2 F/SI
Crashes)
Bike Not Stated, MV
Making Left Turn
(1 Crashes, 1 F/SI
Crashes)
Hit Object
(23 Crashes, 2 F/SI
Crashes)
Relative
Direction of
Travel
1 Perpendicular
(23 Crashes, 8 F/SI
Crashes)
Perpendicular
(14 Crashes, 4 F/SI
Crashes)
Perpendicular
(67 Crashes, 7 F/SI
Crashes)
2 Unknown
(6 Crashes, 1 F/SI
Crashes)
Same
(15 Crashes, 0 F/SI
Crashes)
Same
(71 Crashes, 5 F/SI
Crashes)
3 Same
(7 Crashes, 0 F/SI
Crashes)
Opposite
(6 Crashes, 0 F/SI
Crashes)
Solo
(29 Crashes, 3 F/SI
Crashes)
Violation
Type
1 Failure to yield to
pedestrian at
intersection
(13 Crashes, 2 F/SI
Crashes)
Disregarded signal
(4 Crashes, 2 F/SI
Crashes)
Too fast for conditions
(60 Crashes, 4 F/SI
Crashes)
2 Did not safely stop,
park or reverse on
highway
(7 Crashes, 1 F/SI
Crashes)
Inappropriate turn
(9 Crashes, 1 F/SI
Crashes)
Disregarded signal
(31 Crashes, 4 F/SI
Crashes)
3 Failure to yield to a
vehicle that is
yielding to pedestrian
(1 Crashes, 1 F/SI
Crashes)
Failure to yield while
entering highway
(4 Crashes, 1 F/SI
Crashes)
Failure to yield while
entering highway
(13 Crashes, 3 F/SI
Crashes)

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Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Table 84: HIN Roadways in San Carlos
Roadway name Length (mi)1 Motor Vehicle
HIN2
Bicyclist HIN2 Pedestrian HIN2
Alameda de las Pulgas 6.7 X X X
Belmont Avenue 0.6 X
Brittan Avenue 1.4 X X
El Camino Real 23.5 X X X
Holly Street 1.0 X X
Industrial Road 2.4 X X
Laurel Street 1.5 X
Old County Road 3.4 X X
San Carlos Avenue 1.4 X
Stafford Street 0.4 X
[1] The length value is for the entire HIN corridor, which may represent portions in neighboring jurisdictions. [2] This column identifies whether any portion of the selected roadway is along the HIN (results may differ within select jurisdictions)
Table 85: San Carlos top EPDO intersections
Intersection Name State Highway Network EPDO Score
Motor Vehicle Only
Brittan Avenue / Old County Road N 285
Industrial Road / Brittan Avenue N 251
El Camino Real / Belmont Avenue Y 230
Pedestrian
Brittan Avenue / Cordilleras Avenue N 191
Industrial Road / E San Carlos Avenue N 191
Brittan Avenue / Old County Road N 120

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Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Intersection Name State Highway Network EPDO Score
Bicycle
Alameda De Las Pulgas / Howard Avenue N 202
El Camino Real / Eaton Avenue Y 191
San Carlos Avenue / Devonshire Boulevard N 120

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Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Woodside
The following tables show crash statistics for key variables, locations along the modal HINs, and top intersections based on EPDO. The intersection table is shared for informational purposes and does not represent a potential project list, which will be evaluated and developed as part of a subsequent task. The modal HINs can be viewed dynamically online at this site. 31 Table 86: Crash Statistics for Key Variables, Woodside Variable Rank Pedestrian Bicyclist Motorists
All Crashes 1 Crashes, 1 F/SI
Crashes
72 Crashes, 21 F/SI
Crashes
112 Crashes, 22 F/SI
Crashes
Crash
Location
1 Unsignalized
Intersection
(1 Crashes, 1 F/SI
Crashes)
Segment
(48 Crashes, 12 F/SI
Crashes)
Segment
(89 Crashes, 19 F/SI
Crashes)
2 Unsignalized
Intersection
(22 Crashes, 9 F/SI
Crashes)
Unsignalized
Intersection
(20 Crashes, 3 F/SI
Crashes)
3 Signalized Intersection
(2 Crashes, 0 F/SI
Crashes)
Signalized Intersection
(3 Crashes, 0 F/SI
Crashes)
Crash Type
1 Unknown - Bike
Proceeding Straight,
Ped In Road,
Including Shoulder
(1 Crashes, 1 F/SI
Crashes)
Unknown - Bike
Proceeding Straight,
MV Proceeding
Straight
(1 Crashes, 0 F/SI
Crashes)
Hit Object
(38 Crashes, 10 F/SI
Crashes)
2 Solo - Overturned
(20 Crashes, 8 F/SI
Crashes)
Head-On
(18 Crashes, 6 F/SI
Crashes)
3 Solo - Other
(12 Crashes, 6 F/SI
Crashes)
Sideswipe
(10 Crashes, 4 F/SI
Crashes)
Functional
Classification
1 secondary
(1 Crashes, 1 F/SI
Crashes)
secondary
(30 Crashes, 11 F/SI
Crashes)
secondary
(59 Crashes, 16 F/SI
Crashes)
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Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Variable Rank Pedestrian Bicyclist Motorists
2 tertiary
(11 Crashes, 4 F/SI
Crashes)
(33 Crashes, 3 F/SI
Crashes)
3 (16 Crashes, 3 F/SI
Crashes)
residential
(9 Crashes, 2 F/SI
Crashes)
Lighting
Condition
1 Light
(1 Crashes, 1 F/SI
Crashes)
Light
(72 Crashes, 21 F/SI
Crashes)
Light
(87 Crashes, 15 F/SI
Crashes)
2 Dark
(25 Crashes, 7 F/SI
Crashes)
Posted
Speed Limit
1 45
(1 Crashes, 1 F/SI
Crashes)
45 (42 Crashes, 13 F/SI
Crashes)
45 (64 Crashes, 18 F/SI
Crashes)
2 Unknown
(16 Crashes, 3 F/SI
Crashes)
(33 Crashes, 3 F/SI
Crashes)
3 35
(8 Crashes, 3 F/SI
Crashes)
35 (9 Crashes, 1 F/SI
Crashes)
Pre-Crash
Movement
1 Bike Proceeding
Straight, Ped In Road,
Including Shoulder
(1 Crashes, 1 F/SI
Crashes)
Other
(29 Crashes, 11 F/SI
Crashes)
Hit Object
(37 Crashes, 10 F/SI
Crashes)
2 Overturned
(20 Crashes, 8 F/SI
Crashes)
Other
(45 Crashes, 9 F/SI
Crashes)
3 Bike Proceeding
Straight, MV Making
Left Turn
(4 Crashes, 1 F/SI
Crashes)
MC Proceeding
Straight, MV
Proceeding Straight
(6 Crashes, 2 F/SI
Crashes)

February 5, 2024 Page 141
Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Variable Rank Pedestrian Bicyclist Motorists
Relative
Direction of
Travel
1 Unknown
(1 Crashes, 1 F/SI
Crashes)
Solo
(35 Crashes, 14 F/SI
Crashes)
Solo
(57 Crashes, 11 F/SI
Crashes)
2 Same
(18 Crashes, 4 F/SI
Crashes)
Opposite
(28 Crashes, 9 F/SI
Crashes)
3 Perpendicular
(7 Crashes, 2 F/SI
Crashes)
Same
(19 Crashes, 1 F/SI
Crashes)
Violation
Type
1 Too fast for conditions
(1 Crashes, 1 F/SI
Crashes)
Drivers/cyclists yield to
drivers already in an
intersection
(1 Crashes, 0 F/SI
Crashes)
Too fast for conditions
(51 Crashes, 10 F/SI
Crashes)
2 Disregarded signal
(1 Crashes, 0 F/SI
Crashes)
Driving while under
the influence of
alcohol
(8 Crashes, 4 F/SI
Crashes)
3 Too fast for conditions
(38 Crashes, 13 F/SI
Crashes)
Do not cross double
parallel lines
(10 Crashes, 3 F/SI
Crashes)

February 5, 2024 Page 142
Final Task 3 Existing Conditions Analysis Memo Jurisdiction-Specific Analysis Kittelson & Associates, Inc.

Table 87: HIN Roadways in Woodside
Roadway name Length (mi)1 Motor Vehicle
HIN2
Bicyclist HIN2 Pedestrian HIN2
Cañada Road 7.1 X X
Farm Hill Boulevard 1.8 X
Kings Mountain Road 3.5 X
La Honda Road 14.0 X X
Portola Road 4.2 X X
SR 35 25.3 X X
SR 84 2.6 X
Woodside Road 7.0 X X
[1] The length value is for the entire HIN corridor, which may represent portions in neighboring jurisdictions. [2] This column identifies whether any portion of the selected roadway is along the HIN (results may differ within select jurisdictions)
Table 88: Woodside top EPDO intersections
Intersection Name State Highway Network? EPDO Score
Motor Vehicle Only
Woodside Road / Lindenbrook Road Y 202
Portola Road / Old La Honda Road N 191
La Honda Road / Martinez Road Y 191
Pedestrian
Cañada Road / Corto Lane N 191
Bicycle
La Honda Road / Portola Road Y 404
Skyline Boulevard / La Honda Road Y 202
Woodside Road / Oakhill Drive Y 191

February 5, 2024 Page 143
Final Task 3 Existing Conditions Analysis Memo Next Steps Kittelson & Associates, Inc.

NEXT STEPS
This memo presents hot spot (location based) and systemic (location and systemwide) crash trends. The evaluation can stand alone and provide directly useful information for participating jurisdictions to consider and to incorporate in the Countywide LRSP.

The memo will also serve upcoming tasks in the following ways:
◼ Task 4: Community and Stakeholder engagement – We will use the key findings listed here including the mode-specific and combined HIN to relay safety trends when engaging with the public. We will request their input on whether their experience is reflected in the key findings presented here. Caltrans is involved in the advisory group and we will continue to work with them in this task.
◼ Task 5: Countermeasures/Implementation – We will develop a list of Safe System-aligned recommendations to address and improve safety. The recommendations will be mapped to the key findings of this memo—for example:
– Emphasizing nighttime safety based on lighting and crash risk findings, – Recommending engineering solutions that address the top crash and location types. – Recommending target populations and behaviors based on the demographic/user behavior findings.
◼ Task 7: Develop Prioritized List of Safety Projects for Each Jurisdiction in San Mateo County:

We will use the HIN, top systemic/roadway characteristics and equity spatial data to screen the network and prioritize locations to develop project lists for the county. We will continue to coordinate with Caltrans in this task.
◼ Task 8: Countywide LRSP Development: We will concisely summarize the key findings as part of the plan.

We have shared this memo with the advisory group and others at C/CAG staff’s direction and have addressed one set of non-conflicting comments.

February 5, 2024 Page 144
Final Task 3 Existing Conditions Analysis Memo Appendix Kittelson & Associates, Inc.

Appendix A
Pedestrian HIN Maps by Jurisdiction

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Menlo Oaks
North
Fair Oaks
Sequoia Tract
Stanford
Lands
West Menlo
Park
Atherton
Flood Park
ß/101
¦§¨280
Bay Rd
Santa Cruz Ave
Middlefield Rd
El Camino Real
Selby Ln
Fair Oaks
Ln
Woodside Rd
Sand Hill Rd
Alameda De Las Pulgas
Stockbridge Ave
Jefferson Ave Marsh
Rd
Valparaiso Ave
Atherton Ave
Main St
Alpine
Rd
Willow Rd
H:\28\28717 - C_CAG San Mateo Countywide LRSP\GIS\28717 San Mateo LRSP.aprx Date: 9/4/2023 Pedestrian High Injury Network (HIN)
City of Atherton, CA
Figure P-1
0 0.5Miles [
! Schools
BART Station
Caltrain Station
Unincorporated Places
Parks
Non-Highway Pedestrian HIN
Highway Pedestrian HIN

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Devonshire
Peninsula
Golf And
Country Club
Belmont
Cal. Fish
And Game
Water Dog
Lake Park
Laurelwood
Park
Eaton Park
Sugarloaf
Mtn.

Big
Canyon Park
ß/101
∙þ92
¦§¨280
Crestview Dr
De Anza
Blvd
S El Camino Real
Harbor Blvd
Is
al n
d
Pkwy
42nd Ave
Club Dr
Alameda De Las Pulgas
La Selva St
W 20th Ave
E
d
g
e
w
a
t
er Blv
d
37th Ave
Holly St
Twin Dolphin Dr
E Hillsdale Blvd
G
el n
d
o
ra
Dr
Pacific Blvd
Hacienda St
Brittan Ave
Ralston Ave
S Norfolk St
Edison St
Old County Rd
El Camino Real
S Delaware St
Los
Pra
d
os
St
San Carlos Ave
Laurel St
28th Ave
Shoreway Rd
Marine Pkwy
Franklin Pkwy
Canada Rd
31st Ave
Beach Park Blvd
Saratoga Dr
W Hillsdale Blvd
Bridg
e
P
kw
y
Murphy Dr
Ca
m
pus
Dr
Industrial Way
H:\28\28717 - C_CAG San Mateo Countywide LRSP\GIS\28717 San Mateo LRSP.aprx Date: 9/4/2023 Pedestrian High Injury Network (HIN)
City of Belmont, CA
Figure P-2
0 0.5Miles [
! Schools
BART Station
Caltrain Station
Unincorporated Places
Parks
Non-Highway Pedestrian HIN
Highway Pedestrian HIN