To delve into the characteristics of drivers’ lane-changing behavior within highway interchanges, this study firstly used the Tracker tool to extract vehicle lane-changing trajectory data from videos. Subsequently, a random forest regression model was employed to analyze the most crucial influencing factors of vehicle lane-changing. At last, traditional indicators, TTC and DRAC, along with an accident tree’s risk categorization standards, were used to classify the lane-changing risk. The research results show that lane-changing behavior is most profoundly influenced by the longitudinal spacing with the preceding vehicle after the lane change, with an importance of 21.2%. The refined accident tree safety risk categorization method proves to be more accurate. The results of this study can serve as a methodological reference for drivers’ lane-changing behaviors on highways and offer rational recommendations for future traffic safety management.


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    Titel :

    Research on the Lane-Changing Behavior of Drivers at Highway Interchanges


    Beteiligte:
    Xiang, Wenjun (Autor:in) / Peng, Chen (Autor:in) / Feng, Jian (Autor:in) / Zhang, Tianyue (Autor:in) / Zhu, Jinrui (Autor:in)

    Kongress:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Erschienen in:

    CICTP 2024 ; 2508-2517


    Erscheinungsdatum :

    11.12.2024




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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