Examining Pedestrian Crash Frequency, Severity, and Safety in Numbers Using Pedestrian Exposure from Utah Traffic Signal Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 10
Abstract
The focus of this study was twofold: (1) to estimate models of frequency and injury severity in pedestrian crashes at signalized intersections and (2) to examine whether the “safety in numbers” effect applies to pedestrian safety in the United States. Specifically, the analysis used novel and robust measures of pedestrian exposure: pedestrian crossing volumes estimated from 1 year of pedestrian push-button traffic signal data. Multiple negative binomial models—predicting 10-year pedestrian crash counts at 1,606 signals in Utah—were estimated, accounting for different levels of data availability. The models showed similar results, identifying specific characteristics of the signals that saw more pedestrian crashes. These characteristics were higher volumes of pedestrian and motor vehicle traffic, longer average crossing distances, more crosswalks, continental instead of standard markings, no prohibitions of right-turns-on-red, no bike lanes, near-side instead of far-side bus stops, greater shares of commercial or vacant land uses, more employment density, greater intersection density, no schools or places of worship, and greater shares of people with a disability or people of Hispanic or non-White race/ethnicity. To analyze injury severity in pedestrian crashes, an ordered logit model was fitted with 1,483 pedestrian crash observations. The model results indicated that vehicle size, vehicle maneuvering direction, lighting conditions, and involvement of teenage/older driver and DUI/drowsy/distracted driving in crashes were significantly associated with pedestrian crash severity. Notably, the study also found strong support for the “safety in numbers” effect, in which pedestrian–vehicle crash rates decline with an increase in pedestrian volumes.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was funded by the Utah Department of Transportation as Research Project 19.03.16. Thanks to members of UDOT’s Technical Advisory Committee (Robert Chamberlain, Travis Evans, Heidi Goedhart, Andrea Guevara, Peter Tang, and Mark Taylor) for their guidance and feedback on this work. Acknowledgments are also due to Caleb Child, Devan Peterson, Doo Hong Lee, and Keunhyun Park for their help assembling some of the intersection data. The authors alone are responsible for the preparation and accuracy of the information, data, analysis, discussions, recommendations, and conclusions presented herein. The contents do not necessarily reflect the views, opinions, endorsements, or policies of the Utah Department of Transportation or the US Department of Transportation. Therefore, the Utah Department of Transportation makes no representation or warranty of any kind and assumes no liability.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 10 • October 2022
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© 2022 American Society of Civil Engineers.
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Received: Jan 4, 2022
Accepted: Jun 2, 2022
Published online: Aug 9, 2022
Published in print: Oct 1, 2022
Discussion open until: Jan 9, 2023
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