With the expansion of urban rail transit network, the passenger demand becomes more uneven and complex. An accurate passenger flow forecasting of urban rail transit is important for both operation planning and service quality improvement. This paper proposes a passenger flow forecasting method based on prophet-GRU combined model. Firstly, Seasonal- Trend-Loss decomposition is used to decompose the urban rail transit passenger flow time series into trend, seasonal and residual terms. Then, on the basis of STL decomposition, prophet model is established for the trend and seasonal terms, GRU model is established for the residual terms as well. Taking the Nanjing Metro Line Network as the example, all the prediction results are obtained by linear integration. The results show that the weighted accuracy of passenger flow prediction reaches 96.13% for 159 stations. The root mean square error (RMSE) of the combined Prophet-GRU model is at least 49.69% lower and the mean absolute error is at least 19.00% lower compared to the ARIMA, GRU, and Prophet models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Urban rail transit passenger flow forecasting based on prophet-GRU combined model


    Contributors:
    Ladaci, Samir (editor) / Kaswan, Suresh (editor) / Lu, Yongjiu (author) / Ye, Mao (author) / Zhang, Renjie (author) / Zhao, Yifan (author) / Wu, Yanxi (author) / Guo, Xiaojie (author)

    Conference:

    International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023) ; 2023 ; Xiamen, China


    Published in:

    Proc. SPIE ; 12759


    Publication date :

    2023-08-10





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Urban Rail Transit Passenger Flow Forecasting - XGBoost

    Sun, Xiaoli / Zhu, Caihua / Ma, Chaoqun | TIBKAT | 2022


    Urban Rail Transit Passenger Flow Forecasting—XGBoost

    Sun, Xiaoli / Zhu, Caihua / Ma, Chaoqun | ASCE | 2022



    Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit

    Zhang, Jinlei / Chen, Feng / Cui, Zhiyong et al. | IEEE | 2021


    Forecast and Model Establishment of Urban Rail—Transit Passenger Flow

    Zhang, Dandan / Liu, Zhiyuan | Springer Verlag | 2020