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Bus Fleet Accident Prediction Based on Violation Data: Considering the Binding Nature of Safety Violations and Service Violations

Author

Listed:
  • Tongqiang Ding

    (Transportation College, Jilin University, Changchun 130022, China)

  • Lianxin Zhang

    (Transportation College, Jilin University, Changchun 130022, China)

  • Jianfeng Xi

    (Transportation College, Jilin University, Changchun 130022, China)

  • Yingjuan Li

    (Department of Traffic Management, Jilin Police College, Changchun 130022, China)

  • Lili Zheng

    (Transportation College, Jilin University, Changchun 130022, China)

  • Kexin Zhang

    (Transportation College, Jilin University, Changchun 130022, China)

Abstract

The number and severity of bus traffic accidents are increasing annually. Therefore, this paper uses the historical data of Chongqing Liangjiang Public Transportation Co., Ltd. bus driver safety violations, service violations, and road traffic accidents from January to June 2022 and constructs road traffic accident prediction models using Extra Trees, BP Neural Network, Support Vector Machine, Gradient Boosting Tree, and XGBoost. The effects of safety and service violations on vehicular accidents are investigated. The quality of the prediction models is measured by five indicators: goodness of fit, mean square error, root mean square error, mean absolute error, and mean absolute percentage error. The results indicate that the XGBoost model provides the most accurate predictions. Additionally, simultaneously considering safety and service violations can improve the accuracy of the model’s predictions compared to a model that only considers safety violations. Bus safety violations, bus service violations, and bus safety operation violations significantly influence traffic accidents, which account for 27.9%, 20%, and 16.5%, respectively. In addition to safety violations, the service violation systems established by bus companies, such as bus service codes, can be an effective method of regulating the behavior of bus drivers and reducing accidents. They are improving both the safety and quality of public transportation.

Suggested Citation

  • Tongqiang Ding & Lianxin Zhang & Jianfeng Xi & Yingjuan Li & Lili Zheng & Kexin Zhang, 2023. "Bus Fleet Accident Prediction Based on Violation Data: Considering the Binding Nature of Safety Violations and Service Violations," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3520-:d:1068375
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    References listed on IDEAS

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    1. Yafeng Gu & Li Deng, 2022. "STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting," Mathematics, MDPI, vol. 10(9), pages 1-16, May.
    2. Farbod Farhangi & Abolghasem Sadeghi-Niaraki & Seyed Vahid Razavi-Termeh & Soo-Mi Choi, 2021. "Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale," Sustainability, MDPI, vol. 13(18), pages 1-25, September.
    3. Maowei Chen & Lele Zhou & Sangho Choo & Hyangsook Lee, 2022. "Analysis of Risk Factors Affecting Urban Truck Traffic Accident Severity in Korea," Sustainability, MDPI, vol. 14(5), pages 1-14, March.
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    Cited by:

    1. Yingcui Du & Feng Sun & Fangtong Jiao & Benxing Liu & Xiaoqing Wang & Pengsheng Zhao, 2023. "The Identification of Intersection Entrance Accidents Based on Autoencoder," Sustainability, MDPI, vol. 15(11), pages 1-17, May.

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