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Guide to Pedestrian Analysis (2022)

Chapter: Chapter 3 - Pedestrian Safety Analysis

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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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Suggested Citation:"Chapter 3 - Pedestrian Safety Analysis." National Academies of Sciences, Engineering, and Medicine. 2022. Guide to Pedestrian Analysis. Washington, DC: The National Academies Press. doi: 10.17226/26518.
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29   In 2017, 5,977 pedestrians died in traffic crashes. Although this was a slight decrease from 2016, the general trend since 2009, when 4,109 pedestrians died, has been ever-increasing numbers of pedestrians being killed in traffic crashes. Pedestrian fatalities accounted for 16% of all fatalities in motor vehicle traffic crashes in 2017 (1). As a result, improving pedestrian safety is an important part of transportation agencies’ efforts to work toward eliminating fatalities and serious injuries in the use of transportation systems. Transportation agencies require the following information when they are prioritizing pedestrian safety improvements and selecting suitable safety countermeasures for high-priority locations: • Pedestrian exposure data. Exposure is a “measure of the number of potential opportunities for a crash to occur” (2, p. vi). It is used to determine risk, “the probability of a crash to occur given exposure to potential crash events” (2, p. vii). Considering both exposure and risk is an important part of identifying and prioritizing locations for installing safety counter- measures. Pedestrian volume data—whether obtained from actual counts or estimated as described in Chapter 2—are key elements in determining exposure and risk. This chapter describes current best practices for estimating pedestrian exposure. • Guidance on prioritizing locations for treatment. With data in hand about pedestrian crashes and crash rates, transportation agencies can screen locations to prioritize the implementation of safety countermeasures. This chapter describes three approaches to identifying candidate locations for treatment: (a) one based on pedestrian safety performance (a crash-based approach), (b) one based on pedestrian crash risk (a systemic approach), and (c) a combination of the two (a hybrid approach). This chapter presents strengths and weaknesses of each approach and identifies resources for further information. • Estimates of the effect of safety countermeasures on crashes. Once a location has been identified as a candidate for the installation of safety countermeasures, potential counter- measures can be evaluated in terms of their crash-reduction potential. This chapter identi- fies resources for identifying the range of countermeasures that are potentially applicable to a location, along with current knowledge about the crash-reduction potential of selected countermeasures. • Estimates of the effect of safety countermeasures on perceived safety and comfort. Install- ing a countermeasure that pedestrians do not perceive to be sufficiently safe and, consequently, do not use wastes resources. Therefore, it is also important to evaluate a countermeasure’s effect on QOS, that is, how well a pedestrian facility operates from the pedestrian’s perspec- tive. Countermeasures that improve motorist yielding rates improve pedestrian satisfac- tion with their crossing experience (3). This chapter summarizes current knowledge about motorist yielding rates associated with different countermeasures. The higher the motorist yielding rate, the less delay that pedestrians experience. A method for quantifying pedestrian delay is presented in Chapter 4. Chapter 5 presents a method for quantifying pedestrians’ satisfaction with an uncontrolled crossing. C H A P T E R 3 Pedestrian Safety Analysis

30 Guide to Pedestrian Analysis Estimating Pedestrian Exposure This section presents resources for estimating pedestrian exposure. It provides examples of risk assessment methods that can be applied to different geographic scales and roadway system ele- ments. More information is available in the following documents, which are referenced throughout this section: • FHWA’s Synthesis of Methods for Estimating Pedestrian and Bicyclist Exposure to Risk at Areawide Levels and on Specific Transportation Facilities (4) defines exposure and risk, and documents methods in the literature to quantify pedestrian exposure and risk. It defines geographical scales that include points (e.g., intersections, midblock crossings), segments (e.g., street sections between major intersections), networks (transportation facilities in a limited geographic area such as a census tract or traffic analysis zone), and regions (transportation facilities in a large area such as a city, metropolitan area, county, or state). The companion Guide for Scalable Risk Assessment Methods for Pedestrians and Bicyclists (2) provides guidance on estimating pedestrian exposure and risk for these various geographic scales. • “Estimating Pedestrian Accident Exposure: Protocol Report” (5), prepared for the California DOT (Caltrans), provides guidance on measuring exposure for different purposes (e.g., evaluating the safety effects of pedestrian countermeasures, comparing risk between popula- tion groups or travel modes) and different geographical scales. Each combination of purpose and scale is accompanied by unique needs and challenges that must be addressed according to the study definition of exposure as well as in the design of the methodology for data collection and analysis. Definitions and Concepts This subsection summarizes the basic definitions and concepts for exposure and risk. The Guide for Scalable Risk Assessment Methods for Pedestrians and Bicyclists defines exposure as “a measure of the number of potential opportunities for a crash to occur” (2 p. vi) and risk as the number of crashes of a particular type or severity divided by exposure. Although exposure is simple to define, there has been no agreement in the literature on how to measure it (2), which reflects the large variety of geographical scales at which exposure can be measured and the amount and kind of data available at each scale. Data that can readily be obtained for a single intersection may be impractical to obtain at a statewide level, thus requiring the development of different measures of exposure suitable to the data type available at the analysis scale. The following categories of exposure measures can be defined (4): • Population-based. Exposure is measured by the population of an area or, where a travel demand survey is available, the population of an area that walks regularly. • Trip-based. Exposure is measured by the number of walking trips made in an area. • Volume-based. Exposure is measured by using the pedestrian and/or motorized traffic volume along a facility or crossing at an intersection. • Distance-based. Exposure is measured in terms of the total length traveled by pedestrians; for example, pedestrian miles traveled along a facility or crosswalk length at an intersection. • Time-based. Exposure is measured in terms of the time spent by persons while walking; for example, person hours of travel along a facility or time to walk across a street intersection. Table 3-1 summarizes applications and considerations for each of these exposure measures. Potential Purposes of Exposure Studies Exposure estimates are an important part of safety analyses and prioritizing and assessing pedestrian projects. For example, a city can track pedestrian crashes over time, but, without

Consideration Basis for Defining Exposure Population Trips Volumes Distance Time Appropriate uses Areawide analysis when detailed information about pedestrian activity is infeasible to collect Assessing pedestrian and bicyclist behavior in large areas; walking trip common characteristics Compare exposure at the areawide level, i.e., for a specific jurisdiction Estimating pedestrian volume and risk at a specific location Comparison of exposure at the micro level, i.e., for specific transportation facilities Estimating exposure at micro and macro levels Estimating whether pedestrian risk increases with distance traveled Assessing how crossing distance affects risk Estimating exposure at micro and macro level Estimating whether pedestrian risk increases linearly with walking time Comparing risk between travel modes Comparing risk between different length crossings and individuals with different walking speeds Data sources American Community Survey: population by segment Travel demand surveys showing propensity to make walking trips on a regular basis Travel surveys Manual or automated counts Travel surveys Manual or automated counts of pedestrians, combined with the length of the specific area or corridor of interest Travel surveys Manual or automated counts of pedestrians and the measurement of the time traveled Advantages Easy to obtain and low-cost data available for most geographic regions Can adjust for differences in the underlying resident population of an area Vehicular volume likely to be related to area population Only way to represent exposure if no direct measurements are available Appropriate for use in large areas Best metric for assessing relationship of walking with trip purpose Trips can be assessed as a function of person, household, and location attributes Relatively simple to collect as opposed to measures such as distance or time Data collection can be costly if done for longer durations Automated methods for counting are improving over time More information than manual or automated pedestrian counts alone Can be used to measure exposure at micro and macro levels Common measure of vehicle exposure More information than manual or automated counts alone Can be used to measure exposure at micro and macro level Accounts for the traveler speed and different paths taken by the traveler to reach the destination Allows for accurate comparison between travel modes Disadvantages Does not accurately represent levels of pedestrian activity Does not account for distance or time that pedestrians are exposed to traffic Does not accurately represent levels of pedestrian activity Does not provide enough detail needed to assess risk at specific locations Trip-based measures are not meaningful for facility-specific geographic scales Does not differentiate by walking speed, age, or other factors that may influence individual risk Does not account for time or distance walked Does not account for exposure over a macro level, i.e., city, county Relatively difficult to collect data Assumes risk is equal over distance traveled Does not account for traveler speed or different paths taken by the traveler Relatively difficult to collect data Assumes risk is equal over entire time traveling Time spent is overestimated Trips are underreported, i.e., short trips are usually forgotten by people Common measures Number of people in an area, potentially segmented by age, gender, race, socioeconomic status, and so on; number of people in an area who walk regularly Number of trips, possibly by purpose Number of pedestrians per time period; number of people crossing; average daily, weekly, or annual pedestrian volume; product of pedestrian and vehicle volumes (interactions) Total or average miles traveled per pedestrian, total or average miles crossed per pedestrian Total or average amount of time spent traveling, total or average time taken by pedestrian crossing an intersection Source: Adapted from “Estimating Pedestrian Accident Exposure: Protocol Report” (5) and Guide for Scalable Risk Assessment Methods for Pedestrians and Bicycles (2). Table 3-1. Broad categories of exposure definitions.

32 Guide to Pedestrian Analysis considering exposure, cannot evaluate whether relative risk has changed. Have more crashes occurred because more people are walking, or have more crashes occurred even though walking activity has remained comparatively steady? Exposure data can be used to • Develop pedestrian crash rates for a facility or geographic area; • Assess pedestrian safety trends over time and the eectiveness of safety countermeasures; • Assess crash rates on the basis of metrics such as time of day, land use density, socioeconomic characteristics, gender, or facility type; • Conduct cost–benet analyses of safety improvements; • Develop crash modication factors (CMFs) for safety countermeasures; and • Develop safety performance functions (SPFs) for dierent vehicle–pedestrian crash and location types. For example, Toronto, Canada, maintains a database of signalized intersections that includes vehicle and pedestrian volumes. Combined with site characteristics such as the number of intersection legs and data on vehicle–pedestrian crashes, the database can be used to assess risk in terms of collisions per 100 million entering vehicles and 100 million crossing pedestrians, as illustrated in Figure 3-1 (6). As another example, the Michigan DOT developed a tool to produce pedestrian and bicycle crash risk scores by bicycle or pedestrian analysis zone (BAZ or PAZ, respectively—a 400-meter ¼-mile] by 400-meter area) to help inform the installation of countermeasures (7). e risk scores were based on estimated safety performance functions and observed crashes. e Michigan DOT used risk scores to estimate exposure on the basis of the number of daily walking or bicycling trips in each BAZ/PAZ, which could then be applied at the analysis zone or segment level. Figure 3-2 illustrates pedestrian risk at the PAZ level and pedestrian exposure at the segment level. Exposure Scale and Coverage e literature emphasizes the importance of scale in estimating exposure. Exposure scale is dened as the most granular geographic level for which an exposure measure is desired. How- ever, scale is dierent from coverage, in that coverage denotes the total geographic area included in an exposure analysis (4). e Guide for Scalable Risk Assessment Methods for Pedestrians and Bicyclists (2) applies the following geographic scales: • Facility-specic scales: – Street crossing (intersection or midblock). Example: e number of pedestrians crossing an intersection and the number of vehicles conicting with pedestrians can be used to estimate exposure for each crossing movement. – Road segment (between intersections). Example: e number of pedestrians crossing a midblock location, where exposure is estimated on the basis of crossing distance. Source: NCHRP Web-Only Document 129 (6). Figure 3-1. Excerpt from a database used to estimate signalized intersection risk.

Pedestrian Safety Analysis 33   • Areawide scales: – Network (trac analysis zone, census tract, census block group). Example: e number of pedestrian crashes in a census tract can be compared with the total population of the census tract. – Regional (city, county, metropolitan area, or state). Example: e number of walking commuters or the number of pedestrian fatality rates per million population in a state. Analysts should consider how to dene pedestrian exposure to crash risk. Most denitions of exposure incorporate direct conict between pedestrians and motor vehicles that might occur at intersections where pedestrians must cross the travel lanes used by motor vehicles. However, in places without road-separated, parallel pedestrian facilities, “walking along road” crash types may be common. us, analysts should consider including crash type in pedestrian exposure estimates. Typical Data Needs Once an analyst has developed a sense of where and when pedestrians are exposed to crash risk at geographic scales of interest, the next step is to gather data about vehicle–pedestrian crashes. Listed below are examples of critical and supplemental data worth compiling to carry Source: Michigan DOT Pedestrian and Bicyclist Safety Risk Assessment Tool (8). Figure 3-2. Example of Michigan DOT’s Pedestrian and Bicyclist Safety Risk Assessment Tool.

34 Guide to Pedestrian Analysis out robust pedestrian safety analyses adapted from Road Safety Fundamentals: Concepts, Strate- gies, and Practices that Reduce Fatalities and Injuries on the Road (9). • Critical data: – Vehicle–pedestrian crashes, including location, time, and severity; – Traffic volumes; – Some measure of pedestrian exposure to crash risk, as described in the next section; and – Road characteristics. • Supplemental data: – Traffic citation data (e.g., speeding, drivers failing to yield to crossing pedestrians); – Vehicle–pedestrian conflicts and avoidance maneuvers; – Sight distance at intersections and driveways; – Injury surveillance and emergency medical systems data on pedestrian injury; – Law enforcement operations and observations data; – Public survey on perceptions of pedestrian safety; – Direct field observation data, including from pedestrian safety assessments or road safety audits; – Sociodemographic data (U.S. Census), e.g., population and employment densities; – Travel behavior data (travel diaries and surveys, including the NHTS); – Transit data (stop locations, boardings/alightings, routes); – Infrastructure data (including related infrastructure such as lighting and buffers); – Locations of pedestrian attractors; – Sidewalk and path locations and conditions; – Sidewalk physical and effective (i.e., usable) widths; – Crosswalk dimensions; – Traffic signal timing for pedestrians; and – Output from transportation demand models. Crash data are the most widely used type of safety data and are essential in road safety analysis. Nevertheless, pedestrian crash data present challenges, including human error in reporting, unreported pedestrian crashes (10), and the length of time it can take for crashes to be entered into a database. When the safety-related impacts of implemented countermeasures are evaluated, the number of crashes or crash rates are often the primary measure: • Crash frequency: number of crashes occurring per year or other unit of time. • Crash rates: numbers of crashes normalized by a population or metric of exposure. Commonly cited crash rates include crashes per 100,000 people living in a state, city, or country. Other crash rates present crash numbers per miles traveled or per licensed drivers. Crash out- comes can be measured by the types of injuries sustained by the people involved in the crash and are typically categorized by fatalities and injury severity. Increasingly, agencies monitor fatal and serious injury crashes to guide the prioritization of safety projects (9). Local law enforcement crash records are typically a good source of information on the vehicles and people involved in crashes, the road user movements that immediately preceded the crash, and the consequences of crashes such as fatalities, injuries, property damage, and citations (9). Methodologies for Estimating Exposure There are many ways to estimate pedestrians’ exposure to crash risk. The methodology used to estimate exposure will depend on the availability of relevant data, whether agencies have access to statistical software, and the desired geographic scale of analysis, ranging from entire cities, regions, or states down to individual facilities. Toolbox 3-1 provides methodologies for assessing exposure at both the areawide and facility levels.

Pedestrian Safety Analysis 35   Sketch Planning—Areawide Analysis Sketch planning includes methods for estimating exposure that are simple to apply and provide an alternative to complex models. They may be implemented in a spreadsheet or geographic information system (GIS) and incorporate travel survey data. The methods primarily depend on the available data (e.g., nationally collected survey data) and require little effort in terms of data collection and no specialized expertise. They typically use simple computations, rules of thumb, and population estimates. References and Resources (to name a few): Carter et al. (9), Harmon et al. (10), National Association of City Transportation Officials (11), Salon (12), Schneider and Stefanich (13), Blaizot et al. (14), Chu (15), Jacobsen (16). UNITS OF EXPOSURE Population Distance traveled Number of commuters who walk Number of persons who regularly make walking trips Time spent traveling DATA SOURCES NHTS American Community Survey Regional travel surveys GEOGRAPHIC SCALE City, county, metropolitan area, state, country ADVANTAGES Utilizes data that are available Includes simple computations and estimations Creates simple and practical solutions Requires limited resources Does not require specialized expertise DISADVANTAGES Relatively low accuracy Challenging to validate Mostly aggregated estimates EXAMPLES The National Association of City Transportation Officials (11) used data from the American Community Survey to assess the risk of injury or death to cyclists. The analysis was also conducted at a city level for a variety of locations in the United States. A study used regional household travel survey and crash data to estimate exposure on the basis of the number of trips, distance traveled, and travel time. Injury rates were disaggregated on the basis of location and demographic characteristics, e.g., density, gender, and age (14). (continued on next page) Identifying Treatment Locations With pedestrian exposure and the crash and roadway context data in hand, an analyst can turn to identifying and prioritizing locations for treatment. Transportation agency safety programs typically use one of three approaches to identify and prioritize locations for safety treatments: • Crash-based, focusing on locations with high numbers or rates of crashes, • Systemic, focusing on locations with similar characteristics with the greatest potential to prevent future crashes, and • Hybrid, combining elements of both the crash-based and systemic approaches. Toolbox 3-1. Methodologies for assessing exposure at areawide and facility levels.

36 Guide to Pedestrian Analysis Network Analysis Model – Specific Transportation Facilities Network analysis models are much more complex than sketch planning models and are based on a pedestrian network representation. They typically use a four-step modeling approach for trip generation and distribution. Space Syntax is one of the most well-known examples of network analysis models and was first developed in the mid-1980s in London. These models are used to estimate volumes for specific facility types (e.g., street segments or intersections) over an entire area of interest such as a neighborhood or city. Beginning with collection of base data and ending with forecasting future pedestrian volumes on the basis of network changes, seven steps create a Space Syntax predictive model. References and Resources: Raford and Ragland (17). UNITS OF EXPOSURE Average annual daily pedestrian traffic DATA SOURCES Manual counts Census data GEOGRAPHIC SCALE Point ADVANTAGES Good detail Reasonable accuracy Limited data requirements Useful for estimating pedestrian flows along corridors Applied widely in Europe and Asia Appropriate to urban volume analysis DISADVANTAGES Relatively unused in the United States Model must be calibrated with pedestrian counts Requires existing GIS data Model should undergo sensitivity testing Process is not intuitive (does not follow traditional trip generation and distribution steps) EXAMPLE A study applied the Space Syntax model to estimate pedestrian volumes at intersections in Oakland, California. The output volumes were then used in a safety analysis for the city’s first pedestrian master plan (17). e following subsections describe each approach, along with how they are oen best used in combination. In addition to the references cited below, the Highway Safety Manual, 2nd ed., in production at the time of writing, is expected to provide extensive information on methods for identifying safety treatment locations. Crash-Based Approach Crash-based (“hot-spot”) approaches use screening techniques to identify locations where crashes appear to cluster on the road network. is approach is attractive because it does not neces- sarily require much data beyond pedestrian crash data and typically addresses locations that tend to attract public and media attention. Although not solely focused on pedestrian safety, several of the U.S. cities that were early adopters of Vision Zero, such as New York and San Francisco, have drawn upon a crash-based approach to develop “high injury network” maps (24, 25). Toolbox 3-1. (Continued).

Pedestrian Safety Analysis 37   Direct Demand Model—Specific Transportation Facilities Direct demand models are among the most widely used tools for pedestrian volume estimation and modeling. These models are also used as primary tools in measuring pedestrian exposure in safety analysis. These models are very similar to aggregate demand models, although the analysis is performed at a larger level in aggregate models. References and Resources: Hankey and Lindsey (18), Radwan et al. (19), Molino et al. (20), Qin and Ivan (21). UNITS OF EXPOSURE Weekly crossing pedestrian volume Million pedestrians per unit of time Pedestrian volumes 100 million miles traveled DATA SOURCES Manual counts Automated counts Population and land use data Crossing distances Vehicle average daily traffic GEOGRAPHIC SCALE Point, segment ADVANTAGES Can calibrate to observed pedestrian counts Detailed Utilizes available data Limited sample size required DISADVANTAGES Does not capture behavioral structure Not easily transferable EXAMPLES A study developed a Poisson log-linear regression model to estimate pedestrian counts at signalized intersections. The independent variables in the model included land use variables and day characteristics. With this model, the total number of pedestrian miles traveled were estimated, representing exposure (20). A study estimated a generalized linear regression model that used number of lanes, area type, and sidewalk system as independent variables. The dependent variable was the weekly pedestrian crossing volume, representing pedestrian exposure in safety analysis (21). Most commonly, existing crash data are used in combination with roadway and volume data in GIS soware to identify potential locations for safety treatments. FHWA’s Guidebook on Identication of High Pedestrian Crash Locations (26) describes a process for conducting a crash-based pedestrian safety analysis: • Select analysis scale: – Points (intersections) – Segments – Facilities – Area – System • Select performance measures: – Crash frequency—count or density – Crash rate Toolbox 3-1. (Continued). (continued on next page)

38 Guide to Pedestrian Analysis Discrete Choice Model—Specific Transportation Facilities Discrete choice models utilize information about crossings and crossing behavior to model pedestrian crossing behavior. Crash risk exposure can be estimated for any location along a pedestrian trip where a pedestrian interacts with a vehicle (i.e., a location where a pedestrian is likely to cross). Thus, these discrete choice models are used to develop pedestrian behavior choice models for each location along an entire trip. References and Resources: Lassarre et al. (22), Papadimitriou et al. (23) UNITS OF EXPOSURE Vehicle volume encountered while crossing Product of vehicle volume and pedestrian volume (interactions) DATA SOURCES Manual counts Manual field surveys GEOGRAPHIC SCALE Segment ADVANTAGES Detailed Highly accurate DISADVANTAGES Relatively few studies Significant initial data requirements Lack of information on nonutilitarian travel EXAMPLES A study designed a nested logit model for developing a hierarchical choice structure between junctions and midblock crossings. The model included origin, destination, traffic characteristics, and pedestrian facilities as independent variables (22). – Crash type distribution – Crash severity distribution – Safety index • Select screening method: – Simple ranking – Sliding window – Peak searching – Grid – Polygons • Assign crashes to network elements: – Crash concentration – Intersection buer • Prioritize sites to receive treatment. e simplest approach to a crash-based screening process—i.e., prioritizing locations on the basis of the number of pedestrian crashes that have occurred—will tend to concentrate resources on locations with the greatest pedestrian activity. It is important to address pedestrian safety at locations where a preponderance of pedestrian crashes have occurred on the network. Nonetheless, a sole focus on the absolute number of crashes may neglect locations where individual pedestrians are at a much higher risk of being involved in a crash. Incorporating exposure into a crash-based screening process addresses this issue by controlling for the number of pedestrians passing through a location. Toolbox 3-1. (Continued).

Pedestrian Safety Analysis 39   A second consideration is that crashes in general, and pedestrian crashes in particular, are relatively rare events. A sharp increase in the number of crashes at a location in a given year is often due to random chance and not necessarily an indication that the location is less safe relative to other similar locations. Analyzing crash data over a longer period of time (e.g., 5 years) can help address these random fluctuations. Additionally, as pedestrian-based safety performance functions become more widely available, it will become more feasible to com- pare a location’s actual safety performance with its expected safety performance on the basis of national, state, or local averages. Systemic Approach Systemic (risk-based) approaches use screening techniques to identify facility types with the greatest potential to reduce crash risk. The systemic approach can be defined as “a data- driven, networkwide (or system-level) approach to identifying and treating high-risk roadway features correlated with specific or severe crash types” (27, p. xii). Safety treatments identified through the systemic approach tend to be more widespread, lower-cost improvements designed to reduce the number of crashes in aggregate rather than to focus resources on a few high- crash locations. Because locations identified for treatment through this approach may not yet have experienced many (or any) severe crashes, it is a more proactive approach because it reduces a location’s crash risk before a problem develops (27). An example of the systemic approach is addressing pedestrian crashes that occur at midblock locations along 4- to 5-lane roadways near transit stops. The systemic approach identifies characteristics associated with crash risk at these types of locations and then identifies loca- tions on the roadway network with these characteristics. Next, safety countermeasures are evaluated for their potential to reduce the risk of the selected crash type. The countermeasure (or package of countermeasures) that best reduces crash risk is then implemented at all the iden tified locations, whether or not they have yet to experience a severe crash. NCHRP Research Report 893: Systemic Pedestrian Safety Analysis (27) describes a process for conducting a systemic pedestrian safety analysis: • Step 1: Define study scope. – Define analysis area (e.g., state, city, corridor), – Identify target facility (e.g., midblock crossings) or location type (e.g., intersection or segment), and – Identify subsets of target crash type(s) for systemic focus (e.g., higher-severity nighttime crashes, left-turning motorist versus pedestrian). • Step 2: Compile data. – Compile the data on the roadway, other location characteristics, and crashes that are necessary to identify the risk factors in Step 3. • Step 3: Determine risk factors. – Analyze data to determine factors associated with the target crash type, – Analyze locations of interest (as in a road safety audit, discussed in the section on the hybrid approach below), or – Use alternate approaches from research or local knowledge to identify key risk factors. • Step 4: Identify treatment sites. – Using various screening and ranking methods, identify an optimal set of sites that have common risk and site characteristics suitable for similar packages of treatments. • Step 5: Select potential countermeasures. – Identify appropriate countermeasures or combinations of measures that can potentially address identified risks. – If desired, further refine and prioritize locations identified in Step 4.

40 Guide to Pedestrian Analysis The systemic approach is more data- and analysis-intensive than the crash-based approach, but, at the same time, the data-driven nature of the approach provides a more consistent and equitable process of project selection as compared with addressing individual problems as they arise (27). Hybrid Approach A hybrid approach to identifying locations for treatment involves integrating the strengths of both crash-based and systemic approaches to arrive at a prioritized list of treatment locations on the basis of both historical crash patterns and clusters of risk factors (e.g., higher numbers of lanes, midblock transit stops, skewed intersections). Road safety audits can be incorporated into a hybrid approach by identifying a high crash corridor and then auditing the environment along the corridor, developing countermeasures to improve safety at sites with an established crash history, and proposing roadway changes over a broader area to prevent future crashes due to the factors the auditing process uncovered as risky (28). The Oregon DOT employs a hybrid approach to screen the state’s transportation network. Using the crash-based approach, Oregon DOT staff identify priority corridors for both pedes- trian and bicycle improvements on the basis of crash frequency and severity over 5-year periods. Next, a systemic approach is used to develop a similar list of corridors by identifying the presence of the risk factors. The Oregon DOT’s All Roads Transportation Safety program splits its safety improvement funds evenly between the two types of project prioritization: crash-based (“hot spot”) and systemic analyses (27). SPFs and the empirical Bayes method, as descried in the Highway Safety Manual (29) rep- resent a hybrid approach to traffic safety analysis. SPFs are used to predict crash frequency at a defined location given a set of site-specific conditions. The empirical Bayes method is used to weight the observed crashes at a site and compare them with the predicted crashes from an SPF to estimate the site’s expected long-term crash experience (30). Selecting Pedestrian Safety Countermeasures Once locations have been identified for treatment through one of the selection processes described above, the next analysis step typically involves evaluating and selecting appropriate countermeasures to reduce the frequency and severity of pedestrian crashes. This section provides an overview of the selection process, provides examples of common safety counter- measures, discusses approaches to match countermeasures to identified risk factors, and pre- sents information on the effectiveness of various countermeasures. Overview Drawing upon one of the approaches to identification of treatment locations outlined above, the analyst will have gathered information on crash-contributing factors (e.g., high traffic speeds) and common crash types (e.g., left-turning vehicles versus pedestrians). Each factor provides insight into selecting one or more countermeasures to address the identified issue. According to FHWA’s PEDSAFE: Pedestrian Safety Guide and Countermeasure Selection System (31), pedestrian safety countermeasures can be classified into eight broad categories: • Along the roadway (e.g., sidewalks, multiuse paths), • At crossing locations (e.g., curb ramps, curb extensions, raised crossings), • Transit access (e.g., bus bulb-outs), • Roadway design (e.g., road diets, lane narrowing), • Intersection design (e.g., roundabouts, modified T-intersections),

Pedestrian Safety Analysis 41   • Trac calming (e.g., minicircles, chicanes, speed tables), • Trac management (e.g., street closures, le-turn prohibitions), and • Signs and signals [e.g., LPIs, rectangular rapid-ashing beacons (RRFBs)]. Aer the analyst performs a network screening procedure to identify locations with either relatively low pedestrian safety performance (i.e., a crash-based approach) or high pedestrian crash risk (i.e., a systemic approach), he or she can evaluate a number of context-appropriate safety countermeasures for potential implementation. Countermeasure selection should be informed by potential risks to pedestrian safety under certain conditions. at is, pedestrian safety risk will vary substantially according to pedestrian and trac volumes, motor vehicle operating speeds, land use context, and the number of travel lanes pedestrians must cross. Counter- measures should be selected with these trac, roadway, and land use elements in mind. In Figure 3-3, from the Guide for Improving Pedestrian Safety at Uncontrolled Crossing Loca- tions (32), potential countermeasures can be identied on the basis of given sets of roadway congurations, vehicle AADT levels, and posted speed limits. As the table shows, high-visibility crosswalks can be considered in nearly all contexts. However, in conditions with more travel *Refer to Chapter 4, ‘Using Table 1 and 2 to Select Countermeasures,’ for more information about using multiple countermeasures. **It should be noted that the PHB and RRFB are not both installed at the same crossing location. Source: Guide for Improving Pedestrian Safety at Uncontrolled Crossing Locations (32 ). Figure 3-3. Example countermeasure identication matrix.

42 Guide to Pedestrian Analysis lanes, higher vehicle AADT, and speed limits, the following measures can also be considered: visibility enhancements such as advanced yield or stop lines; pedestrian refuge islands; and actuated pedestrian signals, including RRFBs and pedestrian hybrid beacons. Once potential countermeasures have been identified, they can be evaluated for their ability to address the safety issue being studied. This step is discussed later in this section. Countermeasure Examples This section provides examples of pedestrian safety countermeasures for which research- based information about their effects on pedestrian safety was available at the time of writing. As indicated in the previous section, many other countermeasures can also be considered. These examples are adapted from NCHRP Research Report 893 (27), with additional informa- tion about CMFs incorporated from the FHWA-supported Crash Modification Factors Clearing- house (33). A CMF indicates the estimated change in the number of crashes of a particular type and severity that may result after a countermeasure is implemented. For example, a CMF of 0.80 indicates that the number of crashes is estimated to be reduced by 20% as a result of a counter- measure, while a CMF of 1.20 indicates that the number of crashes is estimated to increase by 20%. As seen in the examples in Table 3-2, CMF estimates for specific countermeasures can vary widely. It is important to consider that engineering countermeasures interact with land use, demographic, and other factors to produce safety effects. Further, as travel patterns change in keeping with social trends (e.g., rising use of SUVs, ride hailing, and package delivery services; shifts in commuting origins and destinations), the CMFs of countermeasures can also change over time. Moreover, crashes themselves are but one important indicator of safety. Vehicle speed is a key determinant of the severity of vehicle–pedestrian collisions. The fatality risk at an impact speed of 50 km/h (about 30 mph) is twice the risk at an impact speed of 40 km/h (25 mph) and five times that at 30 km/h (20 mph) (34). Therefore, although CMFs have yet to be developed for some of the pedestrian safety countermeasures shown in Table 3-2, their potential to improve pedestrian safety lies in their ability to reduce vehicular speeds by adding friction to the driving environment, by enhancing the visibility of pedestrians, or both. Matching Countermeasures to Risk Factors With knowledge of the available suite of pedestrian safety countermeasures, the analyst can begin to evaluate a potential countermeasure’s effectiveness for context-specific risk factors. Pedes- trian safety risk can be thought of as a composite of crash-contributing factors and crash types. Contributing factors to pedestrian crashes often include a high density of driveways along a roadway segment, high motor vehicle speeds and volumes, and poor pedestrian facility conditions (e.g., cracked or raised sidewalks, significant potholes in pedestrian crossings) (52). Other potential contributing factors include the state of crash-involved drivers and pedestrians, such as levels of alcohol or drug impairment, distraction, and demographic factors including age and gender. In identifying factors that may have contributed to a vehicle–pedestrian crash, the analyst can examine police-provided crash diagrams or other available contextual information to consider the following (32): • Vehicle speed; • Driver and pedestrian compliance with regulations and traffic devices; • Pedestrian crossing behaviors; • Human factors related to sight distance and the density of distractions in the environment (e.g., signs, signals, noise);

Pedestrian Safety Analysis 43   Countermeasure CMF or Other Estimated Pedestrian Safety Benefit Example High-visibility crosswalk— vertically arranged street markings designed to improve the visibility of the crosswalk as compared with traverse parallel lines. 0.52 in urban locations (35) 0.63 for high- visibility yellow/green markings in urban school zones (36) In both studies, high- visibility markings replaced standard parallel markings. Source: pedbikeimages.org/Dan Burden. Raised crosswalk/speed table—an elevated section of pavement with a marked crosswalk to encourage drivers to slow down. 0.55 (37) for areawide traffic calming Source: pedbikeimages.org/Brandon Whyte. Table 3-2. Examples of pedestrian safety countermeasures. • Built environment or land use area type; • Intersection presence and types of trac control devices; • Pedestrian crossing distance; • Time of day/day of week/seasonal factors; • Alcohol impairment on the part of pedestrians or drivers; • Distraction on the part of pedestrians or drivers; • Demographics; • Special populations, such as school-aged children, older adults, and persons with disabilities; • Presence of transit stops; and • Density of driveways along a segment or corridor. (continued on next page)

44 Guide to Pedestrian Analysis Table 3-2. (Continued). Countermeasure CMF or Other Estimated Pedestrian Safety Benefit Example Median crossing (refuge) island— a protected space placed in the center of the street to facilitate pedestrian crossings by allowing pedestrians to cross only one direction of traffic at a time. 0.68–0.71 (installation of raised median) (38–40) Source: pedbikeimages.org/Carl Sundstrom. In-roadway “Yield to Pedestrian” sign (R1-6) installed as a gateway treatment—R1-6 signs placed at a crosswalk along the edge of the road and on all lane lines, thus requiring drivers to slow down to drive between two signs. No CMFs yet available. Motorist yielding has been highest with a gateway configuration (41). Speed reductions in some applications (42, 43). Source: Hochmuth and Van Houten (41). Pedestrian hybrid beacon (HAWKa signal)—a traffic control device used to stop motor vehicle traffic to allow pedestrians to cross safely. aHigh-intensity activated crosswalk. 0.31 (44) 0.45 (38, 39) 0.43 for pedestrian hybrid beacon plus advance stop or yield line (38, 39) Source: pedbikeimages.org/Mike Cynecki.

Pedestrian Safety Analysis 45   Countermeasure CMF or Other Estimated Pedestrian Safety Benefit Example Leading pedestrian interval— provides pedestrians with a 3- to 7-second head start when entering an intersection relative to the green signal for parallel vehicular traffic. 0.41–0.95 (45–47) Source: Kittelson & Associates, Inc./Paul Ryus. Rectangular rapid-flashing beacon—user- actuated amber LED blocks that supplement warning signs at unsignalized intersections or midblock crosswalks. They can be manually activated by pedestrians using a push button or passively activated by a pedestrian detection system. 0.53–0.64 (38, 48) Source: pedbikeimages.org/Lara Justine. Sidewalk—a paved path for pedestrians set along the side of a roadway. 0.26 (49) Source: pedbikeimages.org/Dan Burden. Table 3-2. (Continued). (continued on next page)

46 Guide to Pedestrian Analysis Countermeasure CMF or Other Estimated Pedestrian Safety Benefit Example Curb extension—an extension of the pedestrian space at intersections designed to increase the visibility of crossing pedestrians and reduce their crossing distance No CMFs yet available; nonetheless, speed reductions in some applications (50) Source: pedbikeimages.org/Kristin Langford. Pedestrian lighting—human- scale lights that illuminate spaces where pedestrians walk along and across roadways No CMFs yet available; improvements in visibility and driver yielding in some applications (51) Source: pedbikeimages.org/Ron Bloomquist. Table 3-2. (Continued).

Pedestrian Safety Analysis 47   Crash Type Candidate Countermeasure Midblock. The pedestrian walked or ran into the roadway at an intersection or midblock location and was struck by a vehicle. The motorist’s view of the pedestrian may have been blocked until an instant before the impact. Provide curb extensions. Provide a raised pedestrian crossing. Improve/add nighttime lighting. Multiple threat/trapped. The pedestrian entered the roadway in front of stopped or slowed traffic and was struck by a multiple-threat vehicle in an adjacent lane after becoming trapped in the middle of the roadway. Relocate bus stop to far side of crossing area. Provide raised crosswalks to improve pedestrian visibility. Install traffic signals if warranted, including pedestrian signals. Turning vehicle. The pedestrian attempted to cross at an intersection, driveway, or alley and was struck by a vehicle that was turning right or left. Install raised median and pedestrian crossing island. Use traffic-calming devices, such as a raised intersection, to reduce vehicle speeds. Modify skewed intersections. Walking along roadway. The pedestrian was walking or running along the roadway and was struck from the front or behind by a vehicle. Provide a sidewalk on both sides of road. Construct and maintain sidewalks and curb ramps to be usable by people with disabilities. Add sidewalks, install bicycle lanes or painted shoulders, reduce the number of lanes (e.g., from four lanes to three lanes), and add planting strips. Backing vehicle. The pedestrian was struck by a backing vehicle on a street, in a driveway, on a sidewalk, in a parking lot, or at another location. Provide clearly delineated walkways for pedestrians in parking lots. Remove unnecessary driveways and alleys. Relocate pedestrian walkways. Table 3-3. Common pedestrian crash types and candidate safety countermeasures. Moving closer to the precise time and location of vehicle–pedestrian crashes, the analyst can discern the sequence of road user movements that immediately lead up to the crash, otherwise known as identifying crash types. FHWA’s PEDSAFE: Pedestrian Safety Guide and Counter- measure Selection System (31) identifies common pedestrian crash types and candidate safety countermeasures (Table 3-3). Assessing Countermeasure Effectiveness Crash Reduction A countermeasure’s effect on reducing crashes can be estimated with CMFs and SPFs. CMFs provide an estimate of a countermeasure’s ability to reduce certain types and severities of crashes (e.g., severe injury and fatal pedestrian crashes, left turn versus pedestrian crashes, all crashes) following installation. SPFs estimate the average number of crashes at a particular location on the basis of certain characteristics present at the location (e.g., traffic volume, traffic speed). SPFs are used alone or in conjunction with the location’s crash history to estimate long-term crash frequency for baseline conditions (without treatment), while CMFs are applied to estimate the average number of crashes following treatment. Analysts can use SPFs to simulate the effect that introducing speed management countermeasures (e.g., traffic calming, posted speed limit reductions, lane width reductions) may have on severe pedestrian crashes. This SPF-informed CMF selection process can prove useful when there are multiple alternatives to address safety concerns and it is desirable to quantify and compare the potential benefits of each candidate treatment. FHWA’s Quick Start Guide to Using CMFs (53) and the American Association of

48 Guide to Pedestrian Analysis State Highway and Transportation Officials’ Highway Safety Manual, 1st ed. (29), provide more information about selecting and applying CMFs and SPFs. NCHRP Project 17-84, “Pedestrian and Bicycle Safety Performance Functions for the High- way Safety Manual,” in progress at the time of writing, is developing detailed guidance on esti- mating pedestrian SPFs and calibrating them to local conditions. The Highway Safety Manual, 2nd ed., in production at the time of writing, is expected to provide expanded guidance on pedestrian safety analysis relative to the first edition. Motorist Yielding Many countermeasures intended for implementation at pedestrian crossings improve motorist yielding rates. Table 3-4 summarizes the results of a number of studies on motorist yielding. As indicated by the large range of observed yielding rates in Table 3-4 for many countermea- sures, motorist yield rates are influenced by a range of factors. These factors include roadway geometry, travel speeds, isolated versus corridor- or citywide pedestrian crossing treatments, local culture, and law enforcement practices (71). In nearly all cases reported in the literature, safety countermeasures improved yielding rates at a given site as compared with the “before” condition (e.g., crosswalk markings only). Practitioners should supplement or replace these values with local knowledge and engineering judgment. Furthermore, decisions to install a par- ticular treatment should also consider the treatment’s effect on safety and whether site-specific conditions make the treatment inappropriate for that location. Pedestrian Satisfaction Providing safety countermeasures at pedestrian crossings also improves pedestrians’ satis- faction with their crossing experience. The study team conducted surveys of actual pedestrians using treated and untreated uncontrolled pedestrian crossings (3). After the participants’ age, race, income, gender, trip purpose, number of travel lanes crossed, posted speed limit, and traffic volumes were controlled for, the findings confirmed that pedestrians’ satisfaction was strongly correlated with (a) whether drivers yielded to them and (b) whether they had to wait to begin Crossing Treatment Motorist Yielding Rate (%) Sample Size (sites)Average Range No treatment (unmarked) 24 0–100 37 Crosswalk markings only (any type) 33 0–95 58 Crosswalk markings plus: Pedestal-mounted flashing beacon 35 12–57 2 Overhead sign 26 0–52 2 Overhead flashing beacon (push-button activation) 51 13–91 14 Overhead flashing beacon (passive activation) 73 61–76 29 In-roadway warning lights 58 53–65 11 Median refuge island 60 0–100 21 Pedestrian crossing flags 74 72–80 6 In-street pedestrian crossing signs 74 35–88 17 RRFBs 82 31–100 64 School crossing guard 86 — 1 School crossing guard and RRFB 92 — 1 Pedestrian hybrid beacon (HAWK) 91 73–99 37 Midblock crossing signals, half signals 98 94–100 13 Source: NCHRP Web-Only Document 312 (3), compiling data from References 3 and 54–70. Table 3-4. Motorist yielding rates associated with different crossing treatments.

Pedestrian Safety Analysis 49   crossing (which is related both to driver yielding and whether a suciently long gap in trac existed when the pedestrian arrived). e presence of particular countermeasures also improved pedestrian satisfaction beyond just the countermeasure’s eect on pedestrian delay. Of the countermeasures studied at uncontrolled crossings, median islands with RRFBs provided the largest improvement in pedestrian satisfaction, followed in descending order by RRFBs, median islands, marked crosswalks only, and unmarked crosswalks. Finally, pedestrian satisfaction with the crossing task decreased as AADT increased. Appendix A provides a method for estimating pedestrian satisfaction at uncontrolled pedestrian crossings. e study team also surveyed pedestrians at signalized intersection crossings with LPIs and at comparable signalized crossings without LPIs. ere was no statistically signicant dierence in pedestrian satisfaction associated with the presence or absence of an LPI; however, satisfaction was strongly associated with the le-turning vehicular volume over the crosswalk during the pedestrian phase. Because signalized crossing satisfaction was found to dier between survey cities and because the study sites were limited to the types of high-activity urban locations where LPIs might be installed, more research is needed to better understand pedestrian satis- faction with signalized pedestrian crossings (3). Other research has also investigated pedestrian satisfaction with crossing treatments, oering the following ndings: • Road diet (reducing number of vehicle through lanes) moderately improves satisfaction (72, 73). • Street lighting moderately improves satisfaction (74). • Sidewalks with buer from trac strongly improve satisfaction (73, 75). Summary is chapter presents a variety of resources for performing key steps in a pedestrian safety analysis. Pedestrian volume counts or estimates generated through the methods described in Chapter 2 are an important element for determining pedestrian exposure. Exposure, in turn, is used to control for pedestrian activity when locations on which to focus safety treatments are being identied. Screening methods can identify locations with either relatively low pedestrian safety performance (crash-based approach) or a high pedestrian crash risk (systemic approach), or a combination of the two (hybrid approach). Once potential locations for treatment have been identied, a range of potential safety countermeasures can be evaluated to determine the measure or measures with the highest potential for addressing the identied safety issue. As seen in Figure 3-4, the eectiveness of countermeasures can be evaluated in dierent ways, including eects on reducing crashes, increases in driver yielding rates, and improvements in pedestrian crossing satisfaction. Moreover, as most pedestrian safety countermeasures involve either separating pedestrians and drivers in time or space or reducing motor vehicle speeds, countermeasures designed to improve pedestrian safety also tend to enhance driver safety. When countermeasures improve driver yielding rates, pedestrian crossing delay is reduced. A method for quantifying this reduction is described in Chapter 4 and presented in detail in Appendix A. Similarly, a method for quantifying changes in pedestrian crossing satisfaction due to safety countermeasures is described in Chapter 5 and presented in detail in Appendix A. References 1. National Highway Trac Safety Administration. 2019. Trac Safety Facts 2017. Report DOT HS 812 806. U.S. Department of Transportation, Washington, DC. 2. Turner, S., I. Sener, M. Martin, L.D. White, S. Das, R. Hampshire, M. Colety, K. Fitzpatrick, and R. Wijesundera. 2018. Guide for Scalable Risk Assessment Methods for Pedestrians and Bicyclists. Report FHWA-SA-18-032. Federal Highway Administration, U.S. Department of Transportation, Washington, DC. Reduce serious crashes Increase driver yielding Improve satisfaction Figure 3-4. Evaluation of countermeasure effectiveness.

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Roadway designs and signal phasing that address the safety of all road users are being implemented in many cities around the country. As part of this, accurate methods for estimating pedestrian volumes are needed to quantify exposure and, in turn, evaluate the benefits of pedestrian safety measures.

The TRB National Cooperative Highway Research Program's NCHRP Research Report 992: Guide to Pedestrian Analysis presents a state-of-the-art guide to conducting pedestrian traffic analysis on the basis of volume, safety, operations, and quality of service. In addition to the guide, the research provides new evaluation methods for use with the Highway Capacity Manual.

Supplemental to the report is NCHRP Web-Only Document 312: Enhancing Pedestrian Volume Estimation and Developing HCM Pedestrian Methodologies for Safe and Sustainable Communities; two computational engines for implementing the new and updated analysis methods developed by the project: Signalized Crossing Pedestrian Delay Computational Engine and Uncontrolled Crossing Pedestrian Delay and LOS Computational Engine; a Video; five presentations from a peer exchange workshop: Project Overview, Pedestrian Volume Counting, Pedestrian Operations Analysis, Pedestrian Quality of Service Analysis, Pedestrian Safety Analysis, and an Implementation Plan.

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