Nowadays, the phenomenon of congestion in rail transit is becoming more and more severe which need effective passenger inducement measures to alleviate this phenomenon. For solving the problem of precise estimation of passengers in crowded conditions, this article studies from the basis of theories and focuses on interval full load rate. First, a model was designed according to the interval full load rate and the number of passenger-controlled stations to calculate the rail traffic congestion degree. For getting the most accurate prediction results of the congestion degree, Autoregressive Integrated Moving Average model (ARIMA) and Prophet are compared to predict the interval full load rate. Then a simple model was designed to identify the Space-time range of congestion based on full load rate.


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    Titel :

    Passenger Flow State Prediction Based on Full Load Rate under Congestion


    Beteiligte:
    Zhao, Zeyu (Autor:in) / Liu, Jun (Autor:in) / Xu, Xinyue (Autor:in) / Yang, Zhiqiang (Autor:in)

    Kongress:

    21st COTA International Conference of Transportation Professionals ; 2021 ; Xi’an, China


    Erschienen in:

    CICTP 2021 ; 316-327


    Erscheinungsdatum :

    14.12.2021




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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