In order to shorten the tracking interval of urban rail trains and improve the transportation capacity of the line, under the framework of the train control system of vehicle-to-vehicle communication, it is proposed to predict the running trajectory of the preceding train by constructing a time-domain convolutional network model TCN, which provides a basis for the generation of the driving permission of the rear train, forms a tracking mode based on relative position, and finally verifies the logical correctness of the driving permission function design based on the prediction of the preceding vehicle trajectory through the modeling and simulation of the nonferrous Petri network (CPN).


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    Titel :

    Research on tracking interval of urban rail train based on vehicle-vehicle communication


    Beteiligte:
    Easa, Said (Herausgeber:in) / Wei, Wei (Herausgeber:in) / Ba, Lijun (Autor:in) / He, Tao (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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