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Research on Influencing Factors of Urban Road Traffic Casualties through Support Vector Machine

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  • Huacai Xian

    (Transportation and Logistics Engineering College, Shandong Jiaotong University, Jinan 250357, China
    Shandong Key Laboratory of Smart Transportation (Preparation), Jinan 250357, China)

  • Yu Wang

    (Transportation and Logistics Engineering College, Shandong Jiaotong University, Jinan 250357, China)

  • Yujia Hou

    (Transportation and Logistics Engineering College, Shandong Jiaotong University, Jinan 250357, China)

  • Shunzhong Dong

    (Traffic Administration of Shandong Public Security Department, Jinan 250031, China)

  • Junying Kou

    (Traffic Administration of Shandong Public Security Department, Jinan 250031, China)

  • Huili Zeng

    (Transportation and Logistics Engineering College, Shandong Jiaotong University, Jinan 250357, China)

Abstract

Urban road traffic safety has always been vital in transportation research. This paper analyzed the factors influencing the degree of traffic accident casualties on Jinan Jingshi Road and its branch roads, taking them as the study area for urban road traffic safety problems. Additionally, it used the application of Particle Swarm Optimization (PSO), a Support Vector Machine (SVM) model, and a recursive feature elimination (RFE) to rank the contribution degree of the influencing factors. The results showed that driving on rainy days has a high probability of casualties, while the type of collision was a minimum influence factor. Additionally, on rainy days, cars were accident-prone road vehicles, and 8:00–12:00 and 18:00–22:00 were accident-prone periods. Based on the results, preventive measures were further put forward regarding the driver, road drainage capacity, policy management, and autopilot technology. This study aimed to guide urban traffic safety planning and provide a basis for developing traffic safety measures.

Suggested Citation

  • Huacai Xian & Yu Wang & Yujia Hou & Shunzhong Dong & Junying Kou & Huili Zeng, 2022. "Research on Influencing Factors of Urban Road Traffic Casualties through Support Vector Machine," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16203-:d:993630
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    References listed on IDEAS

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    1. Daniel Albalate & Xavier Fageda, 2019. "Congestion, Road Safety, and the Effectiveness of Public Policies in Urban Areas," Sustainability, MDPI, vol. 11(18), pages 1-21, September.
    2. Vahid Najafi Moghaddam Gilani & Seyed Mohsen Hosseinian & Meisam Ghasedi & Mohammad Nikookar, 2021. "Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, May.
    3. Sarbast Moslem & Muhammet Gul & Danish Farooq & Erkan Celik & Omid Ghorbanzadeh & Thomas Blaschke, 2020. "An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
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    1. Junfeng Yao & Longhao Yan & Zhuohang Xu & Ping Wang & Xiangmo Zhao, 2023. "Collaborative Decision-Making Method of Emergency Response for Highway Incidents," Sustainability, MDPI, vol. 15(3), pages 1-23, January.

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