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.
Research on the Lane-Changing Behavior of Drivers at Highway Interchanges
24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China
CICTP 2024 ; 2508-2517
11.12.2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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