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Injury Severity Analysis of Rear-End Crashes at Signalized Intersections

Author

Listed:
  • Mostafa Sharafeldin

    (Wyoming Technology Transfer Center (WYT2/LTAP), Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA)

  • Ahmed Farid

    (Department of Civil and Environmental Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA)

  • Khaled Ksaibati

    (Wyoming Technology Transfer Center (WYT2/LTAP), Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA)

Abstract

Signalized intersections are common hotspots for rear-end crashes, causing severe injuries and property damage. Despite recent attempts to determine the contributing causes to injury severity in this crash type, the frequency of severe rear-end crashes is still significant. Therefore, exploring commonly omitted potential risk factors is essential to proper detection of contributing factors to these crashes and planning appropriate countermeasures. This research incorporated the examination of intersection crash data in Wyoming to examine injury severity risk factors in this crash type. The study examined a set of potential roadway, driver, crash, and environmental risk factors, including pavement surface friction, which is a commonly omitted factor in relevant studies. A random-parameters ordinal probit model was developed for the analysis. The findings demonstrated that two crash attributes (motorcycle involvement and improper seat belt use), three driver’s attributes (driver’s condition, age, and gender), and two environmental and roadway characteristics (road condition and pavement friction) impacted the injury severity of rear-end crashes at signalized intersections.

Suggested Citation

  • Mostafa Sharafeldin & Ahmed Farid & Khaled Ksaibati, 2022. "Injury Severity Analysis of Rear-End Crashes at Signalized Intersections," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13858-:d:952782
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    References listed on IDEAS

    as
    1. Feng Chen & Mingtao Song & Xiaoxiang Ma, 2019. "Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model," IJERPH, MDPI, vol. 16(14), pages 1-12, July.
    2. Mostafa Sharafeldin & Omar Albatayneh & Ahmed Farid & Khaled Ksaibati, 2022. "A Bayesian Approach to Examine the Impact of Pavement Friction on Intersection Safety," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
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    5. Maria Rella Riccardi & Filomena Mauriello & Sobhan Sarkar & Francesco Galante & Antonella Scarano & Alfonso Montella, 2022. "Parametric and Non-Parametric Analyses for Pedestrian Crash Severity Prediction in Great Britain," Sustainability, MDPI, vol. 14(6), pages 1-44, March.
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