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Key Factors Analysis of Severity of Automobile to Two-Wheeler Traffic Accidents Based on Bayesian Network

Author

Listed:
  • Lining Liu

    (Faculty of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China)

  • Xiaofei Ye

    (Faculty of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China)

  • Tao Wang

    (School of Architecture and Transportation, Guilin University of Electronic Technology, Lingjinji Road 1#, Guilin 541004, China)

  • Xingchen Yan

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China)

  • Jun Chen

    (School of Transportation, Southeast University, Si Pai Lou 2#, Nanjing 211189, China)

  • Bin Ran

    (Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA)

Abstract

The purpose of this paper is to analyze the complex coupling relationships among accident factors contributing to the automobile and two-wheeler traffic accidents by establishing the Bayesian network (BN) model of the severity of traffic accidents, so as to minimize the negative impact of automobile to two-wheeler traffic accidents. According to the attribution of primary responsibility, traffic accidents were divided to two categories: the automobile and two-wheeler traffic as the primary responsible party. Two BN accident severity analysis models for different primary responsible parties were proposed by innovatively combining the Kendall correlation analysis method with the BN model. A database of 1560 accidents involving an automobile and two-wheeler in Guilin, Guangxi province, were applied to calibrate the model parameters and validate the effectiveness of the models. The result shows that the BN models could reflect the real relationships among the influential factors of the two types of traffic accidents. For traffic accidents of automobiles and two-wheelers as the primary responsible party, respectively, the biggest influential factors leading to fatality were weather and visibility, and the corresponding fluctuations in the probability of occurrence were 32.20% and 27.23%, respectively. Moreover, based on multi-factor cross-over analysis, the most influential factors leading to fatality were: {Off-Peak Period → Driver of Two-Wheeler: The elderly → Driving Behavior of Two-Wheeler: Parking} and {Drunk Driving Two-Wheeler → Having a License of Automobiles → Visibility: 50 m~100 m}, respectively. The results provide a theoretical basis for reducing the severity of automobile to two-wheeler traffic accidents.

Suggested Citation

  • Lining Liu & Xiaofei Ye & Tao Wang & Xingchen Yan & Jun Chen & Bin Ran, 2022. "Key Factors Analysis of Severity of Automobile to Two-Wheeler Traffic Accidents Based on Bayesian Network," IJERPH, MDPI, vol. 19(10), pages 1-17, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:6013-:d:816145
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    References listed on IDEAS

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    1. Aghaabbasi, Mahdi & Shekari, Zohreh Asadi & Shah, Muhammad Zaly & Olakunle, Oloruntobi & Armaghani, Danial Jahed & Moeinaddini, Mehdi, 2020. "Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 262-281.
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    3. Tao Wang & Sihong Xie & Xiaofei Ye & Xingchen Yan & Jun Chen & Wenyong Li, 2020. "Analyzing E-Bikers’ Risky Riding Behaviors, Safety Attitudes, Risk Perception, and Riding Confidence with the Structural Equation Model," IJERPH, MDPI, vol. 17(13), pages 1-18, July.
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    Cited by:

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    2. Wen Tian & Qin Fang & Xuefang Zhou & Fan Yang, 2022. "The Method of Trajectory Selection Based on Bayesian Game Model," Sustainability, MDPI, vol. 14(18), pages 1-17, September.

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