A sudden increase in passenger flow can primitively lead to continuous congestion of a subway network and thus have a profound impact on the subway system. To prevent the risk caused by sudden overcrowding, the prediction of passenger flow is a daily task of the rail transit management. Most current short-term passenger flow forecasts rely only on inbound passenger flow, which cannot accurately characterize the total impact of sudden passenger flow. To enhance the prediction accuracy, we propose a sudden passenger flow prediction model with two factors, the outbound and inbound passenger flows. The wavelet neural network (WNN) model was used to detect the sudden passenger flow, and subsequently, it is optimized by the genetic algorithm (GA), according to two-factor data characteristics. Sudden passenger flow events from 2014 to 2016 in the Beijing Dongsishitiao Station (DS) were used to train and verify the reliability of the prediction model. The optimized WNN results proved better than the conventional WNN, and the error of models based on two factors was significantly smaller than the models with a single-factor.


    Access

    Download


    Export, share and cite



    Title :

    Subway Sudden Passenger Flow Prediction Method Based on Two Factors: Case Study of the Dongsishitiao Station in Beijing


    Contributors:
    Chengguang Xie (author) / Xiaofeng Li (author) / Bingfa Chen (author) / Feng Lin (author) / Yushun Lin (author) / Hainan Huang (author)


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Short-term subway station entering passenger flow volume prediction method

    LYU LINGLING / HU DELAI / CHANG RUI et al. | European Patent Office | 2023

    Free access

    Short-term Passenger Flow Prediction of Subway Station Based on PSO_LSTM

    Song, Zhenyang / Xu, Jie / Li, Boyu et al. | Springer Verlag | 2022


    Short-term Passenger Flow Prediction of Subway Station Based on PSO_LSTM

    Song, Zhenyang / Xu, Jie / Li, Boyu et al. | British Library Conference Proceedings | 2022



    Subway line network passenger flow prediction method

    SHI YUE / ZHAO GENDANG / SHAN HUAJUN et al. | European Patent Office | 2023

    Free access