Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas

Khondoker Billah, Hatim O. Sharif and Samer Dessouky
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Khondoker Billah: Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Hatim O. Sharif: Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Samer Dessouky: Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA

Sustainability, 2021, vol. 13, issue 12, 1-23

Abstract: Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended.

Keywords: pedestrian; motor vehicle; crashes; fatalities; logistic regression; bivariate analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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