With the continuous expansion of the subway network, timely and accurate short-term passenger flow prediction is of great significance to improve the operation efficiency of the station. From the perspective of historical passenger flow data, wavelet analysis is used to remove the noise of related unconventional fluctuations. Combined with the theory of time series prediction, a suitable model is selected for passenger flow prediction after denoising. Through the example verification, the combined model after wavelet decomposition and reconstruction has high prediction accuracy, which provides the possibility for the practical promotion of short-term passenger flow prediction.


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    Titel :

    Research on Short-Term Passenger Flow Forecast of Urban Rail Transit


    Beteiligte:
    Yu, Li (Autor:in) / Chen, Yingxue (Autor:in) / Liu, Zhigang (Autor:in)

    Kongress:

    13th Asia Pacific Transportation Development Conference ; 2020 ; Shanghai, China (Conference Cancelled)



    Erscheinungsdatum :

    2020-06-29




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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