This paper constructs a passenger traffic volume prediction framework integrating Holt-Winters exponential smoothing model and ARIMA (autoregressive integrated moving average model). Taking the passenger traffic volume historical data of Beijing-Shanghai high-speed railway from July 2011 to June 2017 as an example, we validate the proposed model framework. The results show that the prediction deviation of integrated method is only 0.029, which is lower than that of the two single prediction models. Therefore, the integrated prediction model constructed in this paper is effective.


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    Titel :

    Prediction of High-Speed Railway Passenger Traffic Volume Based on Integrated Method


    Beteiligte:
    Li, Hengda (Autor:in) / Xu, Chang’an (Autor:in)

    Kongress:

    Sixth International Conference on Transportation Engineering ; 2019 ; Chengdu, China


    Erschienen in:

    ICTE 2019 ; 643-650


    Erscheinungsdatum :

    13.01.2020




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch