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To explore the micro-level influencing factors of driving risks for private cars on freeway sections, the study integrates private car trajectory data matched with traffic accidents in both temporal and spatial dimensions. It systematically extracts indicators for driving risk assessment from four aspects: personnel, vehicle, road, and environment. The spatial–temporal range of 5 km upstream and downstream within 5 min before and after the occurrence of accidents was selected for data screening. An XGBoost-based driving risk identification algorithm was established to classify the data, and SHAP values were used for interpretability analysis of the indicators. The results indicate that the developed XGBoost algorithm achieved accuracy rate of 92.5% in identifying driving risks, with certain indicators showing strong interactions and inhibitory effects. The findings provide theoretical support and insights for traffic safety management and risk control.

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Identification and Explanatory Analyses of Driving Risk Factors for Freeway Driven by Trajectory Data

  • 会议论文

  • 发表日期：

    2025年

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  • Bo Xu,
  • Chunjiao Dong,
  • Xiaoya Wang,
  • Penghui Li,
  • Xuedong Yan,
  • Kun Xie

摘要

To explore the micro-level influencing factors of driving risks for private cars on freeway sections, the study integrates private car trajectory data matched with traffic accidents in both temporal and spatial dimensions. It systematically extracts indicators for driving risk assessment from four aspects: personnel, vehicle, road, and environment. The spatial–temporal range of 5 km upstream and downstream within 5 min before and after the occurrence of accidents was selected for data screening. An XGBoost-based driving risk identification algorithm was established to classify the data, and SHAP values were used for interpretability analysis of the indicators. The results indicate that the developed XGBoost algorithm achieved accuracy rate of 92.5% in identifying driving risks, with certain indicators showing strong interactions and inhibitory effects. The findings provide theoretical support and insights for traffic safety management and risk control.