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Detection Method for All Types of Traffic Conflicts in Work Zones

Author

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  • Zhepu Xu

    (Department of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China)

  • Dashan Chen

    (Department of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China)

Abstract

Traffic conflict technology (TCT) is widely used to assess the safety of work zones. The current TCT is temporal and (or) spatial proximity defined based, which can only detect two-vehicle or multi-vehicle conflicts, and is not competent for single-vehicle conflicts. However, single-vehicle accidents in work zones are also severe. This study proposes a detection method for all types of traffic conflicts in work zones. Based on vehicle micro-behavior data, evasive behavior is detected by automatic segmentation, Support Vector Machine (SVM)-based behavior identification, and threshold-based judgment methods. Two-vehicle or multi-vehicle conflicts are detected by classical proximity defined-based method, i.e., the surrogate safety assessment model (SSAM). By comparing the analysis results of the evasive behavior with the one of SSAM, single-vehicle conflicts can be detected. Taking a practical work zone as an example, the effectiveness of this method in detecting all types of traffic conflicts in work zones is verified. The single-vehicle conflict can be subdivided into 10 types according to basic behavior types, such as straight-line driving and decelerating. The two or multi-vehicle conflicts can be subdivided into three types, such as rear-end conflict. The example also verifies the applicability of this method under different traffic volume scenarios.

Suggested Citation

  • Zhepu Xu & Dashan Chen, 2022. "Detection Method for All Types of Traffic Conflicts in Work Zones," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14159-:d:957958
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    References listed on IDEAS

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    1. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
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    2. Ufuk Sanver & Aydin Yesildirek, 2023. "An Autonomous Marine Mucilage Monitoring System," Sustainability, MDPI, vol. 15(4), pages 1-28, February.

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