In recent years, many safety accidents caused by high-density passenger flow have occurred in rail transit hub station at home and abroad. The passenger flow prediction of rail transit can not only provide auxiliary decision for managers in passenger flow management and control, but also can provide theoretical guidance for reasonable allocation of traffic facilities around the stations. This paper combs and analyzes the relevant research on rail transit passenger flow prediction at home and abroad, and analyzes its passenger flow characteristics and reasons based on the AFC data of Shanghai rail transit lines. Then, based on long and short-term memory (LSTM) RNN model, the passenger flow prediction of rail transit is realized, and the predicting effects are very good. This method can not only provide data support for urban rail transit operators to make operation plans, but also provide reference for passengers to choose the right travel time and avoid traffic congestion, and can be applied to other rail transit lines.


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    Titel :

    Characteristics Analysis and Prediction of Rail Transit Passenger Flow Based on LSTM


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Zeng, Xiaoqing (Herausgeber:in) / Xie, Xiongyao (Herausgeber:in) / Sun, Jian (Herausgeber:in) / Ma, Limin (Herausgeber:in) / Chen, Yinong (Herausgeber:in) / Zeng, Xiaoqing (Autor:in) / Yue, Xiaoyuan (Autor:in) / Yuan, Tengfei (Autor:in) / Guo, Kaiyi (Autor:in) / Feng, Dongliang (Autor:in)

    Kongress:

    International Symposium for Intelligent Transportation and Smart City ; 2022 May 20, 2022 - May 21, 2022



    Erscheinungsdatum :

    28.04.2023


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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