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

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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)

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.

Suggested Citation

  • Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2021. "Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas," Sustainability, MDPI, vol. 13(12), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6610-:d:572246
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    References listed on IDEAS

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    1. Young, Jason & Park, Peter Y., 2014. "Hotzone identification with GIS-based post-network screening analysis," Journal of Transport Geography, Elsevier, vol. 34(C), pages 106-120.
    2. Zhongyu Han & Hatim O. Sharif, 2020. "Investigation of the Relationship between Rainfall and Fatal Crashes in Texas, 1994–2018," Sustainability, MDPI, vol. 12(19), pages 1-19, September.
    3. Ferguson, S.A. & Preusser, D.F. & Lund, A.K. & Zador, P.L. & Ulmer, R.G., 1995. "Daylight saving time and motor vehicle crashes: The reduction in pedestrian and vehicle occupant fatalities," American Journal of Public Health, American Public Health Association, vol. 85(1), pages 92-96.
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    Cited by:

    1. Maria Rodionova & Angi Skhvediani & Tatiana Kudryavtseva, 2022. "Prediction of Crash Severity as a Way of Road Safety Improvement: The Case of Saint Petersburg, Russia," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    2. Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2023. "Bivariate-Logit-Based Severity Analysis for Motorcycle Crashes in Texas, 2017–2021," Sustainability, MDPI, vol. 15(13), pages 1-26, June.
    3. Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2022. "How Gender Affects Motor Vehicle Crashes: A Case Study from San Antonio, Texas," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    4. Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2021. "Analysis of Bicycle-Motor Vehicle Crashes in San Antonio, Texas," IJERPH, MDPI, vol. 18(17), pages 1-19, September.

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