Objective To effectively reflect the real-time impact of urban rail transit station staff behavior on evacuation, and to solve the problem that the adoption of predefined static path method for evacuation route planning in VR (virtual reality) environment cannot reveal the dynamic response mechanism of passenger evacuation to station staff immediate behavior, a VR simulation modeling method is proposed to model the intervention of station staff behavior on passenger emergency evacuation path choice. Method The environment modeling method that depicts station physical space as an abstract space recognizable by path algorithm is introduced. The passenger intelligent agent model that can perceive external information and make decisions autonomously is defined, and the exit selection rules based on A* algorithm are formulated. On this basis, a dynamic path planning model is constructed by considering the dynamic change of station staff behavior to the environment and integrating the real-time evacuation route length, the evacuation process time, and the density of pedestrians. A VR simulation system is developed to carry out simulation and analysis using a large-scale comprehensive hub station as example. Result & Conclusion Simulation analysis results show that at the beginning of evacuation simulation, the passenger intelligent agent can formulate global routes based on exist selection. During the process, it can autonomously perform real-time local path planning based on dynamic obstacle state changes caused by station staff, and maintain high responsiveness as the number of passengers in the scenario increases. It indicates that the proposed method can effectively and efficiently capture the dynamic changes in virtual environment and reflect them in the process of passenger evacuation, thereby providing technical support for conducting emergency evacuation VR experiments.


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    Title :

    VR Simulation Modeling for Intervention of Urban Rail Transit Station Staff Behavior on Passenger Emergency Evacuation Path Choice


    Contributors:
    YU Liang (author) / CHEN Feng (author) / WANG Zijia (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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