Passenger flow forecasting is the basis of the rail transit operation department. In the context of the big data era, this paper proposes a new short-term incoming passenger flow forecasting method for rail transit. In this paper, an improved neural network model is trained on the spark distributed parallel computing framework to reduce training time, and a new method to divide data types is proposed. In order to select the input of the model, this paper implements a correlation measure method to characterize the relationship between the influencing factors and the predicted target. Finally, the proposed model is validated by the actual passenger flow data of Guangzhou Metro. The results show that the prediction accuracy of passenger flow is ideal and meets the project requirements.


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    Titel :

    Forecasting Short-Term Entrance Passenger Flow of Urban Rail Transit Stations by the Improved Elman Neural Network


    Beteiligte:
    Bi, Tao (Autor:in) / He, Jiantao (Autor:in) / Ma, Lingling (Autor:in) / Xie, Qiao (Autor:in) / Ye, Mao (Autor:in)

    Kongress:

    19th COTA International Conference of Transportation Professionals ; 2019 ; Nanjing, China


    Erschienen in:

    CICTP 2019 ; 1651-1659


    Erscheinungsdatum :

    2019-07-02




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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