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A Bayesian Approach to Examine the Impact of Pavement Friction on Intersection Safety

Author

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  • Mostafa Sharafeldin

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

  • Omar Albatayneh

    (Department of Civil and Environmental Engineering, School of Natural Resources Engineering and Management, German Jordanian University, Amman 11180, Jordan)

  • 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

The safety of intersections has been the focus of many studies since intersections are considered hazardous zones of road networks. Identifying the main contributing factors of severe traffic crashes at intersections is crucial to implementing appropriate countermeasures. We investigated the major contributing factors to crash injury severity at intersections, particularly pavement surface friction. Nine years of intersection crash data in Wyoming have been analyzed for this study. The random forest technique was employed to identify the importance of critical variables influencing crash injury severity risk. Subsequently, a Bayesian ordinal probit model was applied to examine the relationships between crash injury severity risk and these crash contributing factors. As per the random forest model’s results, pavement friction has a strong impact on crash injury severity risk along with using safety restraints, intersection type, signalized or unsignalized, reckless driving, and crash type. The results of the Bayesian model demonstrated that higher pavement surface friction levels and proper use of restraints reduced the likelihood of severe injury. Based on these findings, several countermeasures may be proposed, such as those pavement friction requirements, driver’s education, and traffic law enforcement to mitigate injury severity concerns at intersections.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12495-:d:930642
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    References listed on IDEAS

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    1. Angelika van der Linde, 2005. "DIC in variable selection," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(1), pages 45-56, February.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. 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.
    2. 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.

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