Practical experience shows that the traditional road safety assessment and improvement measures which are carried out only after the accident can not predict the risks and take active measures in advance. A complete method of representing potential accidents through conflicts to make anticipatory safety judgments is further proposed in this paper. Taking the right turn merging conflict at intersections as the focus, conflict indicators are used to judge the severity of conflicts by Fuzzy C-means clustering based on vehicle trajectory data, and the prediction model of conflict and traffic-related factors is established using BP neural network. This paper puts forward a conflict evaluation system from three aspects of time, space and distance, which can be extended to apply to any type of conflict, and has certain references to the prior control of traffic risks and improvement of traffic safety.


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    Titel :

    Research on right turn merging conflicts at intersection based on vehicle trajectory data


    Beteiligte:
    Easa, Said (Herausgeber:in) / Wei, Wei (Herausgeber:in) / Xie, Ruining (Autor:in) / Cheng, Lin (Autor:in) / Wei, Liying (Autor:in) / Zhou, Xulei (Autor:in) / Zhang, Chenhao (Autor:in)

    Kongress:

    Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023) ; 2023 ; Hangzhou, China


    Erschienen in:

    Proc. SPIE ; 12790


    Erscheinungsdatum :

    07.09.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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