Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.


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    Titel :

    Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model


    Beteiligte:
    Dou, Fei (Autor:in) / Jia, Limin (Autor:in) / Wang, Li (Autor:in) / Xu, Jie (Autor:in) / Huang, Yakun (Autor:in) / Jiang, Xiaobei (Autor:in)


    Erscheinungsdatum :

    2014


    Format / Umfang :

    9 Seiten, 23 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

    Fei Dou / Limin Jia / Li Wang et al. | DOAJ | 2014

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