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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    Title :

    Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model


    Contributors:
    Fei Dou (author) / Limin Jia (author) / Li Wang (author) / Jie Xu (author) / Yakun Huang (author)


    Publication date :

    2014




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown