Predicting rail transit passenger flow is crucial for modifying the metro schedule. To increase prediction accuracy, a model is proposed that combines long short-term memory (LSTM) with single spectrum analysis (SSA). Firstly, a stepwise decomposition sampling (SDS) strategy based on SSA progressive decomposition is proposed as a solution to the data leaking issue in traditional sequence decomposition. Then, based on this strategy, the passenger flow time series with complex features is decomposed into a relatively single trend and fluctuation component. Finally, the LSTM network is employed to perform short-term predictions on each component separately. The predicted value of each component is accumulated to obtain the original passenger flow’ predicted result. The example shows that, compared with the single LSTM and other hybrid models, the proposed method offers a greater overall prediction accuracy in the experimental days, and the method has specific applicability.


    Access

    Download


    Export, share and cite



    Title :

    Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM


    Contributors:
    Haijun Li (author) / Yongpeng Zhao (author) / Changxi Ma (author) / Ke Wang (author) / Xiaoting Huang (author) / Wentao Zhang (author)


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features

    Yang, Dan / Chen, Kairun / Yang, Mengning et al. | IET | 2019

    Free access

    Urban rail transit passenger flow forecast based on LSTM with enhanced long‐term features

    Yang, Dan / Chen, Kairun / Yang, Mengning et al. | Wiley | 2019

    Free access

    Research on Short-Term Passenger Flow Forecast of Urban Rail Transit

    Yu, Li / Chen, Yingxue / Liu, Zhigang | TIBKAT | 2021


    Short-term passenger flow forecast of urban rail transit based on GAN

    Li, Hua / Zhu, Chuang / Li, Haoran | British Library Conference Proceedings | 2023