Accurate short-term passenger flow prediction in urban rail transit (URT) plays an important role in ensuring the stable operation of the URT systems. Because of the complex dynamic spatial-temporal dependencies and potential semantic correlations of the URT network, accurate and effective short-term passenger flow prediction is challenging. To solve these problems, a novel model called the dynamic spatial-temporal graph convolutional network (DSTGCN) was proposed. Firstly, spatial semantic graphs (SSGs) were established to encode the spatial dependencies and semantic correlations of the URT network. Meanwhile, the dynamic graph convolutional network (DGCN) with the spatial attention mechanism was used to learn the dynamic spatial correlations of the nodes in the SSGs. Then, the long short-term memory (LSTM) network was integrated into the DGCN to learn the dynamic changes of passenger flow and capture local temporal dependencies. Moreover, the temporal attention mechanism was introduced after LSTM to capture global dynamic temporal correlations by adjusting the weights of different sequence information. Finally, the full connection layers were used to output the prediction results. Several experiments were conducted on Nanning Metro Line 1 real datasets to evaluate the model. The experimental results showed that the DSTGCN can effectively capture the dynamic spatial-temporal dependencies and semantic associations of the passenger flow. Besides, the prediction performances of the DSTGCN were better than those of existing baseline models, and it can provide technical support for improving the intelligent planning and operation decisions of URT systems.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Learning Spatial-Temporal Dynamics for Short-Term Passenger Flow Prediction in Urban Rail Transit


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Li, Xianwang (Autor:in) / Wu, Jinxin (Autor:in) / He, Deqiang (Autor:in) / Teng, Xiaoliang (Autor:in) / Ren, Chonghui (Autor:in)


    Erscheinungsdatum :

    01.01.2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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

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



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

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


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

    Haijun Li / Yongpeng Zhao / Changxi Ma et al. | DOAJ | 2022

    Freier Zugriff

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

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