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.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model


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


    Publication date :

    2014


    Size :

    9 Seiten, 23 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English







    Forecast of Short-Term Passenger Flow of Urban Railway Stations Based on Seasonal ARIMA Model

    Guang, Zhirui / Yang, Jun / Li, Jian | British Library Conference Proceedings | 2018