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Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model

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Listed:
  • Quan Yuan

    (State Key Laboratory of Automotive Safety and Energy, School of Vehicle & Mobility, Tsinghua University, Beijing 100084, China)

  • Xianguo Zhai

    (Department of Road Traffic Management, Beijing Police College, Beijing 102202, China)

  • Wei Ji

    (Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Beijing 100192, China)

  • Tiantong Yang

    (Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Beijing 100192, China)

  • Yang Yu

    (Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Beijing 100192, China)

  • Shengnan Yu

    (Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Beijing 100192, China)

Abstract

Crashes involving vulnerable road users (VRUs) are types of traffic accidents which take up a large proportion and cause lots of casualties. With methods of statistics and accident reconstruction, this research investigates 378 actual traffic collisions between vehicles and VRUs in China in 2021 to obtain human, vehicle, and road factors that affect the injury severity. The paper focuses on risky behaviors of VRUs and typical scenarios such as non-use of the crosswalk, violation of traffic lights, stepping into the motorway, and riding against traffic. Then, based on the Bayesian General Ordinal Logit model, influencing factors of injury severity in 168 VRU accidents are analyzed. Results demonstrate that the probability of death in an accident will rise when the motorist is middle-aged and the VRU is an e-bicycle rider; the probability of death in an accident will greatly decrease when the VRU bears minor responsibility. Therefore, middle-aged motorists and e-bicycle riders should strengthen safety consciousness and compliance with regulations to prevent accident and reduce injury for VRUs. In addition, helmet-wearing will help to reduce riders’ injuries. This research may provide ideas for intelligent vehicles to avoid collisions with risky VRUs.

Suggested Citation

  • Quan Yuan & Xianguo Zhai & Wei Ji & Tiantong Yang & Yang Yu & Shengnan Yu, 2022. "Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16048-:d:990260
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    References listed on IDEAS

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    1. Dayi Qu & Haiyang Li & Haomin Liu & Shaojie Wang & Kekun Zhang, 2022. "Crosswalk Safety Warning System for Pedestrians to Cross the Street Intelligently," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    2. Lei Yang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Belgacem Bouallegue & Muhammad Faisal Javed & Nermin M. Salem, 2022. "Comparative Analysis of the Optimized KNN, SVM, and Ensemble DT Models Using Bayesian Optimization for Predicting Pedestrian Fatalities: An Advance towards Realizing the Sustainable Safety of Pedestri," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    3. Debela Jima & Tibor Sipos, 2022. "The Impact of Road Geometric Formation on Traffic Crash and Its Severity Level," Sustainability, MDPI, vol. 14(14), pages 1-25, July.
    4. Simone Cordovez & Esteban Ortiz-Prado & Eduardo Vasconez & Felipe Andrade & Katherine Simbaña-Rivera & Lenin Gómez-Barreno & Rich C McIlroy, 2021. "Bicycling-Related Mortality in Ecuador: A Nationwide Population-Based Analysis from 2004 to 2017," Sustainability, MDPI, vol. 13(11), pages 1-10, May.
    5. Haipeng Shao & Jiangping Wang & Yin Wang & Sitian Chen, 2018. "Electric Bicycle Lane-Changing Behavior under Strategy Games," Sustainability, MDPI, vol. 10(9), pages 1-12, August.
    6. Angus Eugene Retallack & Bertram Ostendorf, 2020. "Relationship Between Traffic Volume and Accident Frequency at Intersections," IJERPH, MDPI, vol. 17(4), pages 1-22, February.
    7. Bajada, Thérèse & Attard, Maria, 2021. "A typological and spatial analysis of pedestrian fatalities and injuries in Malta," Research in Transportation Economics, Elsevier, vol. 86(C).
    8. Tanvir Chowdhury & Shakil Mohammad Rifaat & Richard Tay, 2022. "Characteristics of Pedestrians in Bangladesh Who Did Not Receive Public Education on Road Safety," Sustainability, MDPI, vol. 14(16), pages 1-11, August.
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