Passenger flow prediction in urban rail transit is a fundamental task in rail transit construction, and accurate passenger flow prediction can avoid the risk of waste or shortage of rail operation resources. The article proposes to apply XGBoost, an integration-based machine learning method, to rail passenger flow prediction. Taking Xi’an Metro Line 2 as the research object, the swipe card data from April to May 2017 for the whole line was used. The passenger flow characteristics of special holidays, weekdays and rest days were analysed, and the study time period was determined to be a weekday with more concentrated passenger flow distribution. The prediction results of XGBoost were compared and analysed with those of BP neural network model and ARMA model, and it was found that the XGBoost algorithm has higher prediction accuracy and can provide reference for rail transit construction prediction.


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    Titel :

    Urban Rail Transit Passenger Flow Forecasting—XGBoost


    Beteiligte:
    Sun, Xiaoli (Autor:in) / Zhu, Caihua (Autor:in) / Ma, Chaoqun (Autor:in)

    Kongress:

    22nd COTA International Conference of Transportation Professionals ; 2022 ; Changsha, Hunan Province, China


    Erschienen in:

    CICTP 2022 ; 1142-1150


    Erscheinungsdatum :

    08.09.2022




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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