Changing the Future?: Development and Application of Pedestrian Safety Performance Functions to Prioritize Locations in Seattle, Washington

Abstract

This study aimed to use robust analysis methods to identify and screen locations at risk for pedestrian crashes and injuries to help Seattle, Washington, a Vision Zero city, broaden treatment priorities beyond only high-crash locations. For this objective, data from the entire network were used to develop safety performance functions (SPFs) for two pedestrian crash types: total pedestrian crashes at intersections (a high frequency type) and a subset of intersection crashes involving through motorists striking crossing pedestrians (a high severity type). Many variables from roadway, built environment, census, and activity measures were tested. A similar but not identical set of variables, including measures of activity and intersection size and complexity, significantly contributed to crash prediction in both models. Pedestrian volume exhibited a curved relationship to crashes and demonstrated a tendency for expected crashes to begin to decline above a threshold value; however, the causes of this relationship were unknown. The SPFs were used in several ranking methods, including SPF-predicted crashes, empirical Bayes estimated crashes, and potential for safety improvement, to aid in prioritization of locations that might have been candidates for safety improvement but that had not necessarily experienced a high frequency of crashes. On the basis of this example, this approach is feasible for jurisdictions that wish to be more proactive in addressing potential crashes and injuries. Jurisdictions must, however, begin routinely collecting the data needed to implement the method efficiently.

Midblock Pedestrian Crash Predictions in a Systemic, Risk-Based Pedestrian Safety Process

“…These road safety assessments will help to validate the analysis and to identify any risk factors previously missed. A similar process was undertaken for the intersection study described in Thomas et al ( 14 ); the City identified turning movement conflicts and used the results of their observations and the systemic analysis to inform their long-term funding and treatment plans to implement leading pedestrian intervals at intersections with prior observed crashes, and with higher predicted crashes. At the time of writing, SDOT intends to undertake the same type of verification for the current study and will therefore demonstrate the potential for a systemic approach to prioritizing segment-related systemic projects among possible candidate sites.…”

“…Systemic and risk-based analyses, though relatively new in pedestrian safety analysis, have already seen several applications. For example, Thomas et al previously conducted an empirically based systemic safety analysis of intersections in Seattle to identify risk locations for pedestrians ( 14 ). Drawing upon prior risk factor identification and crash analysis, the Arizona Department of Transportation implemented a systemic pedestrian risk program to identify, prioritize, and select sites for treatment through its Arizona Pedestrian Safety Action Plan (PSAP) ( 15 ).…”

Exposure-Based Models of Trail User Crashes at Roadway Crossings

“…The most common variables in these models represent exposure. Higher pedestrian volumes (22)(23)(24)(25), higher bicyclist volumes (26), and higher motor vehicle volumes (22)(23)(24)26) are typically associated with more crashes.…”

“…Physical design and other intersection context variables are also included in some models. After controlling for exposure, models have shown positive associations between pedestrian crashes and signalized intersections (23), roadways with more travel lanes (23,27), rightturn-only lanes (22,28), crosswalks at midblock locations (28), on-street parking (23,24), bus stops (23), nearby commercial land uses (22,24,28), adjacent lower-income neighborhoods (23), and urban areas (25). Models have shown negative associations between pedestrian crashes and raised medians (22,25,27), pedestrian hybrid beacons (25), rapid flashing beacons (25), and advance yield or stop treatments (25).…”

Effect of Built Environment Factors on Pedestrian Safety in Portuguese Urban Areas

“…Central reservationsThomas et al[23], Rankavat and Tiwari[25], De Lavalette et al[30], Damsere-Derry et al [34], Zegeer et al [35], Gårder [36] Pedestrian Infrastructure Sidewalk Ding et al [12], Ukkusuri et al [16], Rankavat and Tiwari [Public lighting Mukherjee and Mitra [22], Lee and Abdel-Aty [27], Assum et al [38], Wanvik [39] Traffic Characteristics Pedestrian traffic volume Elvik [8], Miranda-Moreno et al [9], Chen and Zhou [17], Pulugurtha and Sambhara [18], Thomas et al [23], Dumbaugh and Rae [26], Zheng et al [40] Vehicular traffic volume Elvik [8], Miranda-Moreno et al [9], Pulugurtha and Sambhara [18], Wedagama et al [20], Wier et al [21], Thomas et al [23], Rankavat and Tiwari [25], Lee and Abdel-Aty [27], De Lavalette et al [30], Clifton et al [41], Diogenes and Lindau [42], Landis et al [43], Yagil[44] …”