Achieving Vision Zero: A Data-Driven Investment …docs.trb.org/prp/15-4847.pdfAchieving Vision...

Achieving Vision Zero: A Data-Driven Investment Strategy for Eliminating Pedestrian Fatalities on a Citywide Level

Chava Kronenberg – corresponding author Transportation Analyst

San Francisco Municipal Transportation Agency One South Van Ness Ave, 7th Floor

San Francisco, CA 94103 415.701.4451 (T)

[email protected]

Lucas Woodward Transit Planner

San Francisco Municipal Transportation Agency One South Van Ness Ave, 7th Floor

San Francisco, CA 94103 415.701.4632 (T)

[email protected]

Brooke DuBose Associate

Fehr & Peers 332 Pine Street, 4th floor San Francisco, CA 94104

415.684.7666 (T) [email protected]

Dana Weissman Transportation Planner/Engineer

Fehr & Peers 332 Pine Street, 4th floor San Francisco, CA 94104

415.513.1216 (T) [email protected]

Submission date: August 1, 2014 Total number of words: 5,142 Words + 2 Figures + 5 Tables = 6,892

2 C. Kronenberg; L. Woodward; B. DuBose; D. Weissman

ABSTRACT 1 2 San Francisco completed a robust, data-driven process to define and prioritize pedestrian safety improvement 3 projects; the goal of this process was to identify the lowest cost and most effective strategy to meet a citywide goal 4 of eliminating traffic fatalities. A team of planners, engineers, and epidemiologists examined longitudinal pedestrian 5 collision data to identify the major factors that corresponded with pedestrian-vehicle crashes in San Francisco, 6 focusing on locations with the highest frequency and severity of pedestrian injuries. Collision profiles were 7 developed to classify the most frequent types of vehicle-pedestrian collisions at each intersection. Corresponding 8 pedestrian safety countermeasures, selected for their relatively high effectiveness and low cost, were matched with 9 applicable collision profiles. A scenario planning process was used to formulate a final investment strategy- a 10 prioritized pedestrian safety project list that targeted the specific needs at priority locations. The data-driven capital 11 planning process helped secure funding commitments to implement pedestrian safety projects at 195 intersections 12 over five years, moving San Francisco closer to its policy objective of eliminating pedestrian fatalities. All cities 13 face the challenge of employing rigorous analysis to select and prioritize improvement projects with limited funding. 14 This study created a new, streamlined approach for capital-constrained project development and prioritization that is 15 applicable to all types of safety improvements across any jurisdiction. 16 17 Keywords: pedestrian, safety, Vision Zero, collision analysis, capital planning, investment 18 19 20


INTRODUCTION 1 2 San Francisco is one of the most walkable cities in the U.S., with high rates of walking among residents, workers, 3 and visitors. (1) However, the prevalence of traffic injuries and fatalities in San Francisco constitutes a public health 4 crisis. Pedestrians suffer approximately half of the fatal vehicle-related injuries in San Francisco, compared to 13% 5 nationally. Each year approximately 20 pedestrians are killed and over 800 pedestrians are injured in motor vehicle 6 collisions. (2) These injuries and deaths have high associated social and medical costs; annual pedestrian injury-7 related hospital costs at San Francisco General Hospital alone are estimated at $15 million. (3) To end the trend of 8 injuries and fatalities on San Francisco streets, elected officials partnered with City agencies to adopt Vision Zero in 9 February 2014. Vision Zero is a policy framework for eradicating traffic fatalities, guided by the principle that 10 traffic deaths are never acceptable. The Vision Zero framework includes strategies to address traffic deaths through 11 transportation infrastructure design; in San Francisco, the stated goal of Vision Zero is to fully eliminate traffic 12 fatalities in the City by 2024. 13 To meet the goal of Vision Zero, a collaboration of San Francisco agencies and departments, led by the San 14 Francisco Municipal Transportation Agency (SFMTA), developed a technical approach to deliver a capital-15 constrained prioritized list of pedestrian safety capital projects. The investment strategy emerged from two specific 16 questions: 17 18

How can transportation professionals develop site-specific, data-driven recommendations for a large area 19 that encompasses thousands of intersections? 20

How can capital funds be allocated effectively and efficiently to reduce pedestrian injury in a fiscally 21 constrained environment? 22

23 In San Francisco, $17 million was identified over five years for pedestrian safety improvements starting in July 24

2014. This paper reviews the methodology to best achieve Vision Zero within the constraints of anticipated 25 revenues. 26

The strategy included the following elements: 27 28 Development of collision profiles that classify injury types according to trends in collision factors using 29

longitudinal data. 30 Review and assignment of safety countermeasures that are proven effective and are applicable to the 31

collision profiles. 32 Investment scenario planning that evaluates various investment strategies along multiple criteria. 33 Ranked capital projects with estimated costs for inclusion in the City’s Capital Improvement Program. 34

35 The emerging Vision Zero recommendations were well received by decision-makers, and the success of the project 36 highlights the value of building and utilizing a robust dataset to identify context-specific trends and evaluating 37 collision events in the aggregate. 38 39 LITERATURE REVIEW 40 41 Cities are challenged to employ rigorous, data-driven analysis to select and prioritize pedestrian safety 42 improvements. This study created a new approach for project development and prioritization applicable to all types 43 of safety and streetscape improvements in any city. Although there is no existing precedent for the methodology that 44 this project employed, the work examined a number of resources that helped guide the methods employed. The 45 following literature review examines research and key data used in developing the prioritization methodology. 46 47 Pedestrian Collision Factors 48 49 Infrastructure, traffic behavior, and environmental factors have been found to influence pedestrian-vehicle collision 50 occurrence and severity. The San Francisco Department of Public Health (SFDPH) created a Vehicle-Pedestrian 51 Injury Collision Model to predict changes in the number of severe or fatal collisions associated with area-level 52 changes in street, land use and population characteristics. The model found the following elements to be significant 53


predictors of pedestrian safety need: traffic volume, street classification, land use, land area, employee population, 1 resident population, percent below poverty level, percent aged 65 and older. (4) SFMTA and San Francisco County 2 Transportation Authority developed a Pedestrian Volume Model that used pedestrian counts taken at a variety of 3 locations to estimate annual pedestrian crossings at intersections across the City. The study found that pedestrian 4 volumes were positively associated with number of nearby households and jobs, proximity of high-activity zones 5 with metered on-street parking, fewer hills, proximity to university campuses, and presence of traffic signal control. 6 (5) The New York City Pedestrian Safety Study and Action Plan identified the variables most correlated with 7 pedestrian collisions in that city. The study identified three behavioral issues that principally contributed to 8 pedestrian fatalities and severe injuries: driver failure to yield or inattention, vehicle speed, and pedestrian error or 9 confusion. (6) 10 11 Effectiveness of Pedestrian Safety Countermeasures 12 13 A number of studies have attempted to quantitatively measure injury reduction levels attributable to specific 14 pedestrian safety treatments. The Federal Highway Administration estimated pedestrian crash reductions expected 15 from specific countermeasures or groups of countermeasures for all crash severity types in its Toolbox of 16 Countermeasures and Their Potential Effectiveness for Pedestrian Crashes (2013). The publication summarizes 17 research from fourteen different references, including academic reports and journal articles, and translates their 18 various evaluations into a consistent measurement of expected change in pedestrian crashes across multiple 19 countermeasures. (7) The Pedestrian and Bicycle Information Center’s Evaluation of Pedestrian-Related Roadway 20 Measures: A Summary of Available Research (2013) provides a comprehensive and current review of literature on 21 countermeasure effectiveness evaluation, focusing on countermeasures related to roadway features, crossing 22 locations, transit, roadway design, intersection design, traffic calming, traffic diversion, and signals/signs/traffic 23 control devices. The document compiles an exhaustive list of articles and reports that quantitatively assess 24 pedestrian safety countermeasures, including peer-reviewed journals, presentations, and safety assessments. (8) A 25 collaborative study between the University of California Traffic Safety Center and SFMTA evaluated the 26 effectiveness of pedestrian countermeasures in San Francisco. 27

The data quality and analysis methodologies in the articles varied widely, limiting the ability to consistently 28 compare countermeasures on a quantitative level. (9) 29 30 Pedestrian Safety Countermeasure Costs 31 32 The cost of implementing safety measures is context-sensitive, with costs varying significantly both across 33 implementing agencies and project characteristics. The SFMTA developed unit-level cost estimates from recent city 34 projects as part of the SFMTA biennial Capital Improvement Plan (2013). These cost estimates included varying 35 levels of soft costs, which can include planning, design, environmental and engineering review, traffic control and 36 mobilization, and construction management. (10) A supplemental review conducted by the project team produced a 37 list of unit costs for pedestrian improvement projects recently constructed in the Bay Area. The set of projects 38 included, but was not limited to, the South San Francisco Pedestrian Master Plan (11), Hearst Avenue Complete 39 Streets Improvements in Berkeley (12), and the Emeryville Pedestrian and Bicycle Plan (13). For these projects, soft 40 costs and contingency were estimated as a percentage of total construction costs. The Pedestrian and Bicycle 41 Information Center, in Costs for Pedestrian and Bicyclist Infrastructure Improvements (2013), provided nationwide 42 infrastructure cost estimates based on recent pedestrian and bicycle projects from 40 states and multiple cities across 43 the country. Maximum, minimum, and average costs per facility were provided, along with unit costs. Costs 44 included engineering, design, mobilization, and furnish and installation costs, but they did not include contingency. 45 (14) 46 47 PEDESTRIAN COLLISION DATA 48 49 This work relied entirely on the TransBASE dataset, an inventory of all pedestrian injuries across San Francisco that 50 were caused by a collision with a motor vehicle between 2007 and 2011. The database contains a record for each 51 pedestrian injury and over 200 spatially referenced variables collected from multiple agencies and across a range of 52 geographic scales. Compiled within the database is an extensive set of location-specific information covering factors 53


such as collision characteristics, infrastructure, traffic volumes, zoning, population demographics, health exposures, 1 and presence of neighborhood destinations including businesses, educational institutions, and community amenities. 2 Each pedestrian injury record and spatially referenced variable is linked to an intersection or street segment to 3 enable spatial analysis of the data. (15) 4

Collision records are aggregated in the Statewide Integrated Traffic Records System (SWITRS), a 5 repository of collision data collected by the California law enforcement agencies. Additional data on land use and 6 transportation data came from many sources including but not limited to: United States Census, San Francisco 7 Planning Department, San Francisco Unified School District, SFDPH, SFMTA, and San Francisco Department of 8 Public Works. The SFDPH built and currently maintains the TransBASE pedestrian injury database. 9 10 PROJECT METHODOLOGY 11 The association of pedestrian injuries with land use and transportation characteristics reflects an important 12 hypothesis for transportation planning practice – that pedestrian injuries are not random and unavoidable events, but 13 rather they emerge from specific behaviors and circumstances that can be changed. The project team’s approach for 14 developing a useful methodology to reduce pedestrian injuries was to identify engineering tools to reduce the 15 likelihood and severity of mistakes in the locations where the interventions are needed most. The team developed a 16 high injury network, evaluated pedestrian collision profiles, examined and gave costs to pedestrian safety 17 countermeasures, paired collision profiles to countermeasures, and performed scenario planning to evaluate and 18 choose a final implementation strategy. 19 20 High Injury Network 21 22 With over 1,100 miles of roadway in San Francisco, the identification of safety countermeasures for each specific 23 location would not be feasible or cost-effective. Based on available data, a high injury network was developed to 24 constrain targeted engineering and enforcement efforts to focus on highest need locations and to allow for practical 25 project delivery. 26

High injury corridors were defined by comparing a weighted injury count for each street segment to a 27 threshold. Each pedestrian injury was assigned to an intersection and its adjoining street segments, and an injury 28 count was determined for each street segment. Counts of fatalities and severe injuries were multiplied by three. High 29 injury corridors were defined where proximate street segments yielded a weighted count of nine or greater. These 30 corridors were summarized according to corridor length, fatalities and severe injuries per mile, total injuries per 31 mile, and total weighted injuries per mile. The high injury network is comprised of 87 corridors that represent six 32 percent of San Francisco’s street miles, but 60 percent of total pedestrian injuries during the five-year study period. 33

The aggregation of intersection-level collision data into corridors mitigates inherent year-by-year 34 variability in collision data at a specific location and facilitates pedestrian safety measures that are more effectively 35 implemented on corridors, such as road diets and speed control measures. However, not every concentration of 36 pedestrian injuries fits neatly into corridors. Subsequent analyses preserved “high-injury intersections” for these 37 unique locations. 38 39 Pedestrian Collision Profiles 40 41 San Francisco’s high injury network spans 1,014 intersections. Along the high injury network, 2,314 pedestrian 42 injuries were recorded during the five-year study period, caused by 2,190 pedestrian-motor vehicle collisions. The 43 principal challenge of this project was to address the large numbers of pedestrian injuries and pedestrian injury 44 locations at a scale that would produce site-specific infrastructure recommendations. Pedestrian collision profiles 45 were created to characterize the primary safety issues of a given location, which could be matched with appropriate 46 countermeasures. 47 The project team consulted sources described in the Literature Review to evaluate factors that could 48 potentially contribute to pedestrian-vehicle collisions in San Francisco. These factors included characteristics of the 49 parties involved (e.g., victim age or gender), circumstances leading up to the collision (e.g., driver turning left at a 50 signalized intersection or pedestrian crossing outside of a crosswalk), and environmental conditions (e.g., high 51 traffic volumes or proximity to a senior center). Selected factors were limited to the set of variables included in the 52 pedestrian injury database. 53


Collision profiles reflect the hypothesis that collision factors may act in concert to create likely conditions 1 for pedestrian-vehicle collisions. For example, speeding and failure to yield to a pedestrian frequently occur together 2 in collision records. Collision profiles were also created with an understanding that each must be addressed through 3 effective countermeasures, so profiles were defined with forethought to potential treatments. The constructed list of 4 collision profiles and their primary collision factors informed the project’s subsequent phases of analysis including 5 investment scenario evaluation and project prioritization. Table 1 shows the full set of collision profiles, the primary 6 collision factors associated with each profile, and a brief description of each collision profile. 7 8 TABLE 1 Collision Profiles and Factor Combinations 9 Collision Profile Factor 1 Factor 2 Factor 3 Description

PM VISIBILITY Prevailing lighting

Lack of PUC lighting

Collisions that occurred at dusk or at night where there is less street lighting.

LEFT TURNS AT SIGNALIZED INTERSECTION

Collision involving left turn

Signalized intersection

Collisions that occurred when a motorist made a left turn at a traffic signal.

LEFT TURNS AT SIGNALIZED INTERSECTION WITH ONE-WAY STREET

Collision involving left turn


At least one one-way street

Collisions that occurred when a motorist made a left turn at a traffic signal to or from a one‐way street.

RIGHT TURNS AT SIGNALIZED INTERSECTION

Collision involving right turn


Collisions that occurred when a motorist made a right turn at a traffic signal.

COMPLEX INTERSECTION

5-leg or freeway ramp

Collisions that occurred in intersections with complex travel patterns, such as 5‐leg intersections or freeway ramps.

UNCONTROLLED MARKED CROSSWALK ON ARTERIAL

Marked crosswalk

High speed

None or partial traffic control

Collisions that occurred on major streets at crosswalks without a stop sign or traffic signal.

MID-BLOCK COLLISION

Driver failure to yield ROW or pedestrian failure to cross in crosswalk

Mid-block collision

High vehicle volume

Collisions that occurred at mid‐block locations, with or without a crosswalk.

HIGH SPEED WITH LOW VEHICLE VOLUME

High speed Low vehicle volume

Driver failure to yield ROW

Collisions on streets with high traffic speeds but low vehicle volumes.

PEDESTRIAN CROSSING AGAINST SIGNAL

Pedestrian crossing against red signal

Collisions that occurred when a pedestrian crossed at a traffic signal with a red light or "Don't Walk" signal

PEDESTRIAN CROSSING OUTSIDE XWALK

Pedestrian crossing midblock

Collisions that occurred when a pedestrian crossed outside of a legal crosswalk, marked or unmarked.


Collision Profile Factor 1 Factor 2 Factor 3 Description

PEDESTRIAN DARTING INTO XWALK

Pedestrian outside crosswalk

Collisions that occurred when pedestrians entered a crosswalk too quickly for motorists to yield the right of way.

HIGH RISK FACTORS High violent crime

High vehicle volume

High speed

Collisions that occurred in areas with several challenging pedestrian conditions: high traffic speed, high traffic volume, and a high crime rate.

HIGH PED VOLUME High pedestrian volume

Collisions in areas with the highest pedestrian volumes in the city.

UNSAFE SPEED HIGH VOLUME

High speed High vehicle volume

Driver failure to yield ROW

Collisions on streets with unsafe vehicle speeds and high traffic volumes.

1 Each injury record in the pedestrian injury database was evaluated for inclusion among 14 identified 2

collision profiles, according to the collision factors present. This approach categorized over 90 percent of injuries 3 among one or more collision profiles. Profile designations at the injury level were then aggregated to the intersection 4 level, and the frequency with which each collision profile appeared was calculated for every intersection. The three 5 most frequently cited collision profiles were identified as the relevant collision profiles for a given intersection. 6 Figure 1 provides an example of the maps that were created for every collision profile to illustrate their spatial 7 distribution on the City’s high injury network. 8

9


1 FIGURE 1 Example collision profile map. 2 Pedestrian Safety Countermeasures 3 4 Collision profiles helped to identify the primary causes of pedestrian collisions at each location, allowing the project 5 team to simultaneously generate effective, site-specific recommendations for 1,014 intersections. In order to address 6 the safety need for each collision profile, a set of related engineering safety improvements was identified. Using 7 information from the completed literature review, the team compared the effectiveness of various pedestrian 8 treatments against their costs. The process allowed potential countermeasures to be categorized according to high, 9 medium and low cost and relative effectiveness. Professional engineering judgment and pedestrian safety expertise 10 was applied to pair selected countermeasures with the established collision profiles. With the pairings, site-specific 11 needs could be addressed through cost-effective, targeted pedestrian infrastructure improvements. 12 13 Table 2 provides a summary of the countermeasure effectiveness literature review findings, including a list of 14 examined countermeasures, associated crash modification factor, and other quantifiable measures of effectiveness 15 (e.g., collision reduction, improvement in driver yielding, reduction in speed, reduction in injury severity, increase in 16 compliance, etc.). Crash modification factors (CMFs) are compiled from multiple sources, including the Federal 17 Highway Administration Toolbox (7) and the Crash Modification Factors Clearinghouse (16). Due to uneven data 18 quality and the need to extrapolate from other safety measures like yielding compliance, an ordinal classification is 19 used to compare their effectiveness. 20 21

Evaluation of Pedestrian Countermeasure EffectivenessNotes:

Crash Modification Factor (CMF) is the proportion of crashes that are expected to remain after the countermeasure is implemented, as defined in the FHWA Toolbox of Countermeasures

Impact categories ‐ High, Med, Low, N/A:

‐ High: measurable effect, greater than 10% pedestrian crash reduction if known

‐ Medium: known improved ped experience or driver yielding/compliance (includes speed reduction), less than 10% pedestrian crash reduction if known

‐ Low: possible improvement, unknown impact

‐ N/A = no direct impact on collisions

FHWA Toolbox

SFMTA

CM Summary

PBIC Lit Review

High Med Low N/A

Signalization

Ped countdown heads X X 0.75 22% reduction XReduction in ped collisions noted here (also evaluated for all red‐light‐

running traffic collisions); Sources are consistent

Leading ped phase/Leading pedestrian intervals (LPI) X X X 0.95 12% reduction X

SFMTA, reduction for ped/left‐turn conflicts in NYC; effective for

decreasing collisions and severity, especially with heavy turn volumes

and adopted as CM in NYC, and 3‐second LPI makes ped crossing

easier

Convert permissive or permissive/protected left‐turn

phasingX X 0.57 NA X

SFMTA, reduction applies to all collisions, including vehicle‐vehicle

collisions; improvement noted, but effectiveness not quantified

Flashing beacons (includes RRFB signals) X X ‐‐

20‐94% improvement in driver yielding

behavior; 3‐10% reduction in collisions

for all injury types; significant

reduction in vehicle/ped conflicts and

considered among the most effective

for increasing ped safety

XReferences PEDSAFE and FHWA‐MUTCD; PBIC lit review cites Pedsafe

II project in San Francisco, flashing beacons are highly effective

Pedestrian hybrid beacon (HAWK signal) X X 0.3197% higher yield rate (preventative

measure)X

SFMTA summary focused on yielding compliance; will also be part of

upcoming NCHRP study

Improve signal timing (to match ITE specified intervals); X X 0.63 X

Pedestrian detection to extend crossing time when

pedestrian is detected within the intersectionX ‐‐ X

Increases pedestrian crossing time; similar effectiveness to non‐

automated increase in crossing time

Pedestrian scrambles/ Exclusive ped phasing X X 0.65 34‐80% reduction X

From UC Berkeley and FHWA studies, one study found 34% reduction

in pedestrian collisions, other no change, other 50‐80% reduction in

pedestrian collisions; included here because results vary, but most

studies show decrease in ped collisions

Accessible Pedestrian Signals (APS) X X drop in late ped crossing from 27% to

17%X Focus on decrease in late crossings (not reduction in crashes)

Add new traffic signals at unsignalized intersections when

warrantedX

All crashes ‐

.75X

Geometric

Convert to roundabout X X 0.73 29‐87% reduction in all crashes XEvaluated for fatal/injury crash severity; SFMTA references FHWA,

F&P and TRB

Install raised ped crossing/raised crosswalks/ speed tables

& raised crosswalksX X X

All crashes ‐

.70; Fatal ‐

.64


behaviorX

CMF applies to "All crashes", rather than Ped only; FHWA evaluated

for both all and fatal/injury crashes; Also improves yielding; Effective

at reducing motor vehicle speeds, especially when combined with

overhead beacon; PBIC Lit Review ‐ recommended to enhance

pedestrian safety

X X X 0.44

XAll crashes ‐

.75

X

0.54 ‐

marked

crosswalk

X

0.61 ‐

unmarked

crosswalk

Temporary ‐ painted medians X ‐‐ XReplacing a painted median with a wide raised median reduced

pedestrian crashes by 23% ‐ temporary not as effective as permanent

for this CM

Temporary ‐ Removable pedestrian refuge island with sign

(curb) on two‐lane roadX ‐‐ X

Statistically significant reduction in mean speeds and increase in

speed limit compliance

Corner bulb outs and chokers/ Curb extensions X X ‐‐

14% and 7% decreases in speeds;

reduced overall severity rate,

statistically significant increase in

yielding and increase in yielding

distance

X PBIC Lit Review ‐ recommended to enhance pedestrian safety

Temporary corner bulb outs and chokers X ‐‐ X Assume same effectiveness as permanent

Narrow roadway cross section from 4 to 3 lanes/ Road

dietsX X

All crashes ‐

.7129‐53% reduction X CMF applies to "All crashes", rather than Ped only

Reduced lane width; Lane Narrowing X X ‐‐ X

SFMTA summary ‐ FHWA study indicates change in width by 1 foot

produces 2.5 mph change in vehicle speeds (TRB study indicates that

narrowing lane widths to add lanes increases crashes); PBIC lit review

notes, "No information for this section"

Removal of multiple turn lanes ‐‐ 0 Recommend grouping this countermeasure with road diet

Opening closed crosswalks/ new crosswalk X ‐‐ 0

PBIC lit review ‐ at uncontrolled locations marked crosswalk alone,

compared with unmarked crosswalk, made no statistically significant

difference in pedestrian crash rate; Should be combined with

additional treatments, such as yield‐to‐pedestrian signage and

pavement markings

PBIC Lit Review ‐ recommended to enhance pedestrian safety,

associated with significantly lower rate of ped crashes on multi‐lane

roads with either marked or unmarked crosswalks, especially on

roads with 15,000 ADT

Install refuge islands/raised median/Pedestrian refuge

islands

36% reduction; 23% reduction when

converting painted median to raised

refuge island

X

Research Notes

Countermeasure

Evaluation sources

Crash

Modification

FactorSource: FHWA

Toolbox

Other Quantifiable Measures of

EffectivenessSource: SFMTA CM Summary, PBIC Lit Review

Impact Category

FHWA Toolbox

SFMTA

CM Summary

PBIC Lit Review

High Med Low N/A

Research Notes

Countermeasure

Evaluation sources

Crash

Modification

FactorSource: FHWA

Toolbox

Other Quantifiable Measures of

EffectivenessSource: SFMTA CM Summary, PBIC Lit Review

Impact Category

Curb ramps ‐‐ XIncreases accessibility; ADA compliant curb ramps provide detectable

warning and allow pedestrians to access crosswalks without moving

into line of traffic

Bus bulb outs, and other traffic calming near transit

boarding X X ‐‐ NA 0

Evaluation in SFMTA summary focused on travel time savings; PBIC lit

review notes, "No information for this section" under Transit Stop

Improvements and Access to Transit headings

Chicane No evaluation source

Choker No evaluation source

Rumble strips No evaluation source

Signs, Markings, Operational

Intersection lighting/ Crosswalk lighting X X X

All crashes ‐

.79; Injury ‐

.73

54% reduction at intersections,

79/42% reduction in fatal/injuryX

CMF applies to "All crashes", rather than Ped only; SFMTA, refers to

FHWA and NCHRP, Noted that in SF most high ped volume streets

have lighting, so option would be to enhance lighting, and no data are

available about such improvements; split out from "Roadway Safety

lighting"; PBIC lit review notes that crosswalk illumination increased

driver yielding and driver yielding distance ; PBIC Lit Review ‐

recommended to enhance pedestrian safety, and in particular, a

smart lighting system with pedestrian detection improved visibility

and safety

Segment lighting X X

All crashes ‐

.80; Injury ‐

.77

42% reduction at midblock, 79/42%

reduction in fatal/injuryX

CMF applies to "All crashes", rather than Ped only; SFMTA, refers to

FHWA and NCHRP; Noted that in SF most high ped volume streets

have lighting, so option would be to enhance lighting, and no data are

available about such improvements

Automated speed enforcement (ASE) X 11‐25% reduction in injury collisions XSFMTA summary ‐ Injury crash reductions studied for conspicuous,

fixed camera, ASE programs

No turn on red X XAll crashes ‐

.9757% reduction X

FHWA's CMF applies to "All crashes" (rather than ped only) and

SFMTA summary's 57% reduction applies to ped‐only (other numbers

for other types); Impact category High based on SFMTA summary

reduction

No left turn X X 0.90 10% reduction XSFMTA, 10% reduction in ped collisions; FHWA and SMTA sources are

consistent

Restrict parking near intersections X X X 0.70 33% reduction XSFMTA, average crash reduction for ALL crashes in 6 states, cites FL

DOT; split out from "Advance stop or yield lines/red visibility curbs";

Toolbox and SFMTA sources are consistent

Advance stop or yield lines and Pedestrian yield signs X

67% reduction in conflict with signs,

90% reduction in conflict with sign

AND yield line

X

SFMTA, cites walkinginfo.org; split out from "Advance stop or yield

lines/red visibility curbs"; FHWA evaluates together with warning

signs, SFMTA does not. Research indicates reduction in overall

conflict, but does not specify reduction in collisions

Pavement friction (textured pavement) X 0.97 X Evaluated for fatal/injury crash severity

Right turn pockets 0Noted in feedback from SFDPH, used in the TEP; this may be more

focused on reducing transit delay

Smart lighting No evaluation source

Increase enforcement (along corridors for yielding in

marked crosswalks)X 0.77 X

High‐vis crosswalk (includes continental crosswalks) X X 0.52 X

PBIC Lit Review conflicts with FHWA toolbox ‐ group with all other

crosswalks, as study found no statistically significant difference in ped

crash risk for different crosswalk types (main differences noted when

paired with other treatments such as signs and beacons)

High‐vis crosswalk in conjunction with illuminated

overhead crosswalk signX


behavior (8% at night, 30‐40% during

daylight)

XHigh visibility crosswalks improve crosswalk safety more when paired

with illuminated pedestrian crossing signs

Pedestrian warning signage X Effective for yielding XCompliance noted but change not quantified (80‐90% yielding

overall, not no measure of change)

Speed Control Measures, Miscellaneous

Speed humps and other traffic calming X

25% decrease in speeds, 5‐50%

reduction in crashes on

urban/suburban roads

XAASHTO research variability suggests increase or decrease in

collisions; FHWA evaluation for all crash types and severity types

suggest collision reduction of 40‐50%

Portable Speed Trailer/and Radar speed display signs X

change from 68‐83% driver yielding at

downstream crosswalks, reduced

speeds 1‐6 mph

X From FHWA study

Shared space X Average speed reduction of 8 mph X

Railings and channelization X 37‐44% improvement in driver yielding

behaviorX

Research indicates improvement in driver yielding behavior but does

not specify reduction in collisions

Lower speed limits X range of change in collision rates

depends on difference in speedX

SFMTA, several different studies cite different changes depending on

the speed reduction; the CMF clearinghouse from FHWA suggests

that a reduction of less than 5 mph may increase collisions

Street trees X 0

Texas study found that tree‐lined streets cause motorists to drive

more carefully/slowly; Toronto study found 5‐20% reduction in mid‐

block vehicle collisions in areas with trees or planters; no findings

related to pedestrian collisions

Private vehicle restrictions XHighly effective ‐ vehicle free spaces such as Sunday Streets, evening

events etc. Effectiveness is based on total vehicle restrictions and

assumes full enforcement, not just limits on vehicle streets


The team also considered “temporary” versions of certain engineering countermeasures, such as curb 1 extensions and pedestrian refuges. Using lower-cost materials could allow the City to implement pedestrian safety 2 interventions at more locations, but the lower cost was counterbalanced by an estimated lower effectiveness. 3

Countermeasures were assigned to a high, medium, low or N/A category, as shown in Table 3 below, 4 based on their estimated impact to reduce collision frequency and severity. This facilitated a comparison of their 5 potential impacts on pedestrian safety. Because the differences among treatments were used to select among 6 different capital investment strategies, the precise values of the collision modification factors were less important 7 than their relative valuations. 8 9 TABLE 3 Countermeasure Effectiveness Categories 10 Effectiveness Category Description High Countermeasures have a measurable impact on pedestrian safety and can be credited

with at least a ten percent reduction in crashes Medium Countermeasures improve the pedestrian experience or driver compliance but lead to

a less than ten percent reduction in crashes Low Countermeasures may have quantifiable benefits that are not based on collision

reduction N/A Countermeasure have no known impact on collision reduction 11 Countermeasure Costs 12 13 Costs were developed to facilitate cost-benefit analysis of different investment scenarios and to estimate a total cost 14 for the citywide program. Table 4 provides a summary of the countermeasure cost literature review findings. 15 Countermeasures were categorized according to the relative cost of fully implementing an improvement at a given 16 location: 17

18 Low is less than $10,000; 19 Medium is between $10,000 and $100,000; and 20 High is greater than $100,000. 21

22 By assigning to each countermeasure a high, medium, or low cost category, the countermeasures could be compared 23 for their potential expense of implementation. 24

25 Collision Profile-Countermeasure Pairings 26 27 Based on the findings from the countermeasure effectiveness and cost literature reviews, a set of the most cost-28 effective countermeasures were selected for inclusion in a toolbox of potential pedestrian safety improvements. 29 Countermeasures were assigned to relevant collision profiles in the collision profile-countermeasure matrix shown 30 in Figure 2. Since collision profiles identified the site-specific needs of a given location, a unique set of 31 infrastructure improvements could be recommended to address the needs at each high injury network intersection. 32 33


TABLE 4: Collision Profile and Countermeasure Pairings 1 Selected Collision Profile and Countermeasure Pairings

Facility CMF Appropriate Profiles Cost Effectiveness

Corner Bulbs Unknown Right turns at signalized intersection High pedestrian volumes Complex intersection Uncontrolled marked crosswalk on arterial High Medium

Leading Pedestrian Interval 0.95

Left turns at signalized intersection (one-way) Right turns at signalized intersection High pedestrian volumes Low Medium

Left turn prohibitions 0.9 Left turns at signalized intersection Left turns at signalized intersection (one-way) Medium High

No Right on Red 0.97 Right turns at signalized intersection Low Low

Pedestrian Refuge Islands

0.54 (marked

crosswalk)

Left turns at signalized intersection Left turns at signalized intersection (one-way) Mid-block collision Complex Intersection Uncontrolled marked crosswalk on arterial Medium High


Investment Scenarios and Evaluation Criteria 1 2 The process of defining collision profiles and assigning effective countermeasures generated an extensive set of 3 potential pedestrian improvement projects. The City evaluated these projects within long-term capital constraints. A 4 scenario planning process identified and evaluated possible strategies for targeted pedestrian investments and to help 5 decision-makers understand the trade-offs inherent in the different approaches. The process developed three distinct 6 investment strategies, established criteria to evaluate and compare scenarios, and identified a preferred scenario to 7 guide the City’s development of its pedestrian safety CIP. The result was a discrete set of intersections designated 8 for near-term pedestrian improvement projects and a unique set of countermeasures that targeted the collision types 9 most prevalent at each location. 10

A range of factors were considered when defining the three investment strategies, including geography, equity, 11 cost, and effectiveness. Each of the three investment scenarios prioritized one of these elements: 12 13

A Location-Based scenario focused investments along the high injury corridors with the highest 14 concentration of collisions per mile; 15

A Collision Profile-Based scenario addressed the most severe collision profiles across the city and directed 16 its investments to the locations where those profiles were most prevalent; and 17

A Countermeasure-Based scenario prioritized expediency by favoring countermeasures with the shortest 18 implementation time frame and lowest capital cost. 19

20 Collision Modification Factors were applied to project the reduction of severe and fatal injuries under each scenario. 21 Most intersections received multiple countermeasures; the collision modification factors were multiplied to reflect 22 diminishing marginal returns for multiple treatments. In addition to the projected reductions in severe and fatal 23 injuries, investment scenarios were evaluated according to the capital cost, implementation time frame, and possible 24 controversy or impacts to other modes. Non-infrastructure strategies would complement infrastructure investments 25 in every scenario. 26 27 Preferred Scenario Selection 28 29 Stakeholders reviewed the three candidate investment strategies and agreed on a hybrid approach to guide project 30 prioritization. The preferred scenario targets the highest-injury locations under two project phases. The first 31 prioritized relatively quick and inexpensive treatments for short-term benefits; the second addressed the medium- 32 and long-term needs with more expensive and time-consuming improvements. 33 34 Location Selection 35 36 The preferred scenario focused first on locations along the City’s high injury network with the highest weighted 37 count of injuries. This set of 195 intersections captured 71% of all fatal and severe injuries along the high injury 38 network – a threshold considered adequate because it included the full set of collision types and countermeasures 39 that the City wanted to address. 40

41 Figure 2 shows a map visualizing all selected intersections in the final investment strategy. Proposed 42

projects were focused largely in the downtown core and interspersed at particularly challenging intersections along 43 more peripheral high injury corridors. 44 45


1 FIGURE 2 Intersections selected for final investment scenario. 2 3 Countermeasure Selection 4 5 Phase I: Short-Term Implementation- The City defined five sets of quick and inexpensive countermeasures that 6 would be applied to designated collision profiles at relevant locations. These treatments often included temporary 7 measures made with low-cost materials. A location was eligible for a Phase I countermeasure set if a profile 8 associated with a set was one of the highest-ranked profiles at that location. At each location, the highest-ranking 9 collision profiles were defined as the three profiles with the highest average injury severity level. 10

Table 5 lists the five quick and inexpensive countermeasure sets, including collision profile and 11 corresponding countermeasures. These sets were prioritized for implementation during Phase I of the final 12 investment strategy. 13


TABLE 5 Phase I Countermeasure Sets 1 Collision Profile Countermeasures Left turns at signalized intersections Leading Pedestrian Interval

Temporary medians Turn prohibitions Protected left turns

Right turns at signalized intersections Temporary bulb-outs Leading Pedestrian Interval Turn prohibitions Parking prohibitions

High speed, low vehicle volume Temporary bulb-outs Temporary chokers Temporary medians Speed feedback signs Reduced lane widths Raised crosswalks Traffic circle/roundabouts Speed humps

Unsafe speed, high vehicle volume Speed feedback signs Reduced lane widths Temporary medians Signal timing changes Speed tables

High pedestrian volume Temporary bulb-outs Temporary medians Pedestrian scrambles Leading Pedestrian Interval Turn prohibitions

2 Phase II: Medium to Long-Term Implementation- The second phase of implementation included a full set of 3 countermeasures applied at each location targeted through the intersection selection process outlined above, 4 including those locations addressed with short-term treatments during Phase I. A “full set” was defined as all 5 countermeasures associated with the highest-ranking profiles at a given location, where countermeasure-profile 6 pairings were identified based on the matrix described above. During this stage, all temporary treatments 7 recommended under Phase I were converted to permanent treatments under Phase II. 8

This approach guaranteed that all Phase I locations would receive additional consideration in Phase II to 9 ensure that the quick and inexpensive countermeasures were sufficient to address the top collision profiles at the 10 locations. 11 12 Project Prioritization 13 14 The investment scenario planning process produced a comprehensive list of every countermeasure relevant for each 15 location, based on the collision types present. While a useful planning-level tool, this approach did not incorporate 16 the existing conditions at each intersection studied; potentially recommend improvements could already be in place 17 or be infeasible at a given location. Therefore, SFMTA staff validated the countermeasures at each intersection 18 using existing data sources such as striping drawings, signal timing cards, GIS information, and street view. This 19 produced a subset of countermeasures that could reasonably be implemented. 20

The final outcome was a prioritized list of projects that included each study intersection and its validated 21 set of countermeasures. Project intersections were ranked according to number of fatal and severe collisions, injuries 22 to seniors, injuries to children, and location within a Metropolitan Transportation Commission-designated 23 Community of Concern. The first set of projects within the prioritized list included those locations designated for 24 Phase I treatments, followed by those intersections identified for Phase II treatments, and finally those with 25


improvements that fell outside of the final investment scenario. Projects ranked highest on the prioritized list would 1 be supported through specific funding for pedestrian safety improvements 2 3 OUTCOMES 4

Completion of this study resulted in immediate funding commitments through the SFMTA Capital 5 Improvement Plan of $50 million over five years (an increase of $33 million from initial funding estimates at project 6 onset). The findings and recommendations of WalkFirst became a key component of a balloted local General 7 Obligation Bond for Citywide transportation improvements; San Francisco Proposition A- Transportation and Road 8 Improvement Bond was passed in November 2014. 9

As next steps, all prioritized intersections will be fully vetted in the field with review of site characteristics, 10 pedestrian and motorist behaviors, and any new or additional data before implementing a final selection of 11 treatments. SFMTA planners and engineers are scheduled to implement the first wave of improvements on 153 12 intersections by December 2015. Long-term improvements will begin design in Spring 2015. SFMTA will 13 coordinate with SFDPH to evaluate the outcomes of the interventions. 14 15 CONCLUSIONS AND RECOMMENDATIONS 16 17 This study found that completion of a holistic, data-driven, capital-constrained pedestrian safety planning process 18 could focus safety implementation on proven tools. The authors recommend further opportunities to explore and 19 develop, as highlighted below. 20 21 Build a robust dataset to identify context-specific trends and evaluate independent crash instances in the 22 aggregate. The research benefited from the development of TransBASE, which provided a query-able database that 23 was immediately used by planners and engineers to evaluate long-term collision trends and the environmental 24 conditions that contributed to pedestrian collisions. The team recommends significant time at project onset 25 developing a useful database of existing safety information, including both collision data and environmental 26 characteristics. 27 28 Expand existing data sources. Despite the utility of TransBASE, collision profiles were limited by available data. 29 Future analysis would be enhanced by incorporating additional data, such as more specific information on transit 30 boarding locations (center-islands versus sidewalk stops) and mid-block crossing facilities. Other data sources, such 31 as city speed surveys, are incomplete and necessary to understand the long-term impacts of the implementation of 32 project recommendations. 33 34 Refine methodology for comparing countermeasure efficacy, especially non-infrastructure. This study found 35 that there was limited ability to quantitatively compare countermeasure effectiveness across countermeasures. Future 36 research may refine the ability to compare countermeasures. In particular, collision modification factors are likely to 37 underestimate the effectiveness of tools that have been selected to address specific collision profiles. The City also 38 seeks further research and evaluation on temporary measure installation and effectiveness as compared to their 39 permanent counterparts. Non-infrastructure interventions such as education and enforcement campaigns are 40 potentially highly effective tools, but their effectiveness is difficult to measure. 41 42 Dedicate sufficient time to pairing collision profiles with countermeasures. The engineering exercise of 43 matching collision profiles to specific countermeasures became more influential than the project team initially 44 anticipated. Spending additional time to improve the rigor of this analysis would strengthen future efforts, including 45 regression analysis to determine statistical significance. 46 47 Be aware of state laws prohibiting countermeasures. One countermeasure evaluated by this project, Automated 48 Speed Enforcement (ASE), is currently not permitted in the state of California. As a result of the project findings, 49 the City has begun to seek legislative sponsorship to adopt new state laws regarding ASE implementation in the 50 City. Further research could be completed to strengthen the case for decision-makers to allow for all cities to 51 implement proven safety countermeasures where they are needed. 52 53


Complete a data-driven examination of bicycle collisions and matching countermeasures. This project focused 1 on pedestrian collision data and pedestrian countermeasures to improve safety outcomes for people who walk in San 2 Francisco. Vision Zero also includes safety goals for other modes, including bicyclists. San Francisco should 3 evaluate bicycle safety data through a similar process to reduce injuries and eliminate fatalities for people who 4 bicycle in the City to meet these goals. 5 6 Recognize that effective use of limited funds may be insufficient to meet long-term goals. Focusing on a limited 7 number of intersections and treatments helps move the City towards its goals quickly and within budget, but the City 8 will fall short of its Vision Zero goals without significant additional funding to install permanent measures and long-9 terms streetscape improvements to reduce speed and improve pedestrian facilities and intersections. Performing a 10 capital-constrained exercise helped show the limitations of existing funding for pedestrian safety and helped direct 11 new resources to pedestrian safety initiatives. The prioritized list allowed the City to handily compare the likely 12 collision reductions of two different funding commitments. Further research may consider how to leverage local 13 funding for pedestrian safety for larger, competitive pools of funding, including Federal funding. 14 15 Build partnerships for project and program implementation to ensure long-term success. As part of the 16 project’s planning process, SFMTA worked with stakeholders across the City to evaluate findings and determine 17 next steps. SFMTA will need to continue its collaboration with all partners across the agency and the City, including 18 the Department of Public Works, the SFDPH and the San Francisco Police Department, to ensure that the City can 19 achieve Vision Zero goals, both through infrastructure and complementary non-infrastructure improvements. This 20 relationship will be critical to long-term monitoring and evaluation of the impacts from implementation of the 21 project’s recommendations. 22 23 24 25


REFERENCES 1 1. San Francisco County Transportation Authority, SFCHAMP Model [Software]. 2010. 2 2. California Highway Patrol, Statewide Integrated Traffic Records System, 2007-2011. 3

http://www.chp.ca.gov/switrs/ 4 3. Dicker, R., Max. W., and Lopez, D. Evaluation of Pedestrian Injury and its Associated Hospital Costs in San 5

Francisco. http://sfic.surgery.ucsf.edu/full-research-descriptions/cost-of-pedestrian-injury.aspx Accessed 6 August 28, 2013. 7

4. Pedestrian Injury Model. San Francisco Department of Public Health, Program on Health, Equity and 8 Sustainability. http://www.sfhealthequity.org/elements/24-elements/tools/108-pedestrian-injury-model. 9 Accessed Sep. 20, 2013. 10

5. Schneider, R. J., T. Henry, M. Mitman, L. Stonehill, and J. Koehler. Development and Application of a 11 Pedestrian Volume Model in San Francisco, California. In Transportation Research Record: Journal of the 12 Transportation Research Board, No. 2299, Transportation Research Board of the National Academies, 13 Washington, D.C., 2012, pp 65-78. 14

6. Viola R, Roe M, Shin H. The New York City Pedestrian Safety Study and Action Plan. New York City 15 Department of Transportation: August 2010. 16

7. Federal Highway Administration. Toolbox of Countermeasures and Their Potential Effectiveness for Pedestrian 17 Crashes. Publication FHWA-SA-014. FHWA, U.S. Department of Transportation, 2008. 18

8. Mead, J., C. Zegeer, and M. Bushell. Evaluation of Pedestrian-Related Roadway Measures: A Summary of 19 Available Research. Pedestrian and Bicycle Information Center. April, 2013. 20

9. San Francisco Municipal Transportation Agency. Pedestrian Safety Engineering and Intelligent Transportation 21 System-Based Countermeasures Program for Reduced Pedestrian Fatalities, Injuries, Conflicts, and Other 22 Surrogate MeasuresCountermeasures Effectiveness Summary Memorandum. 2008. 23 http://safety.fhwa.dot.gov/ped_bike/tools_solve/ped_scdproj/sf/. 24

10. Reynolds, S. Low-Cost and Temporary Pedestrian Improvements Memorandum. San Francisco Municipal 25 Transportation Agency. September, 2013. Unpublished. 26

11. City of South San Francisco. South San Francisco Pedestrian Master Plan. City of South San Francisco. 2013. 27 12. Fehr & Peers. Hearst Avenue Complete Streets Study. November, 2012 28 13. Alta Planning + Design and Fehr & Peers. City of Emeryville Pedestrian and Bicycle Plan. May, 2012. 29 14. Bushell, M.A., B.W. Poole, C.V. Zegeer, D.A. Rodriguez. Costs for Pedestrian and Bicyclist Infrastructure 30

Improvements. October, 2013. 31 15. Wier, Megan. TransBASE: Linking Transportation Systems to Our Health. http://www.sfhealthequity.org. 32

Accessed November 14, 2014. 33 16. Federal Highway Administration, Crash Modification Factors Clearinghouse. 34

http://www.cmfclearinghouse.org/ Accessed November 14, 2014. 35 36 37 38

Achieving Vision Zero: A Data-Driven Investment …docs.trb.org/prp/15-4847.pdfAchieving Vision...

Documents

Transcript of Achieving Vision Zero: A Data-Driven Investment …docs.trb.org/prp/15-4847.pdfAchieving Vision...