This paper proposes an improved general regression neural network to predict passenger flow under station closures. First, a random forest classification model is developed to determine the key factors of the reach of a closed station. Then, a generalized regression neural network model is constructed to predict the spatial variation in passenger flow caused by station closures. Furthermore, an improved genetic algorithm is developed to search for optimal model parameters to improve the generalization ability and prediction accuracy of the generalized regression neural network model. A real case study is conducted to verify the proposed approach. Compared with other models, the proposed approach achieves higher prediction accuracy in passenger flow under station closures.


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    Titel :

    Subway Passenger Flow Forecasting Under Station Closure with an Improved General Regression Neural Network


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Liang, Jianying (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Qin, Yong (Herausgeber:in) / Liu, Zhigang (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / An, Min (Herausgeber:in) / Zhang, Ke (Autor:in) / Xu, Xinyue (Autor:in) / Mi, Ziyue (Autor:in)

    Kongress:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Erscheinungsdatum :

    2022-02-19


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch