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A Random Parameters Approach to Investigate Injury Severity of Two-Vehicle Crashes at 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

Roadway intersections are crash-prone locations and, hence, ensuring the safety of road users at intersections has been a major concern for transportation professionals. It is critical to identify the risk factors that contribute to severe crashes at intersections to implement the appropriate countermeasures. Greater emphasis is needed on two-vehicle crashes since they represent the majority of intersection crashes. In this study, a random parameter ordinal probit model was developed to estimate the contributing factors of injury severity of two-vehicle crashes at intersections. Nine years of intersection crash data in Wyoming were analyzed in this model. The study involved the investigation of the influence of a set of intersection, drivers, environmental, and crash characteristics on crash injury severity. The results demonstrated urban and signalized intersections were related to lower severity levels. In addition, higher pavement friction is more likely to be associated with less severe crashes. Crashes that involved drivers who are females or impaired and crashes on weekends were associated with higher severity levels. Intersection crashes that occurred on non-dry road surfaces, in adverse weather conditions, or that involved large vehicles, or out-of-state drivers were less likely to be severe.

Suggested Citation

  • Mostafa Sharafeldin & Ahmed Farid & Khaled Ksaibati, 2022. "A Random Parameters Approach to Investigate Injury Severity of Two-Vehicle Crashes at Intersections," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13821-:d:952247
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    References listed on IDEAS

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    1. 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.
    2. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    3. Sarrias, Mauricio, 2016. "Discrete Choice Models with Random Parameters in R: The Rchoice Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i10).
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    Cited by:

    1. Yoshifumi Wada & Yasushi Asami & Kimihiro Hino & Hayato Nishi & Shino Shiode & Narushige Shiode, 2023. "Road Junction Configurations and the Severity of Traffic Accidents in Japan," Sustainability, MDPI, vol. 15(3), pages 1-17, February.
    2. Zhaoming Chen & Wenyuan Xu & Youyang Qu, 2023. "Joint Analysis of Crash Frequency by Severity Based on a Random Parameters Approach," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
    3. Masayoshi Tanishita & Yuta Sekiguchi, 2023. "Impact Analysis of Road Infrastructure and Traffic Control on Injury Severity of Single- and Multi-Vehicle Crashes," Sustainability, MDPI, vol. 15(17), pages 1-17, September.

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