Traffic flow prediction is a difficult undertaking in transportation systems, due to the intricate periodicity and real-time dynamics for traffic data, spatial-temporal dependency for road networks, existing prediction approaches fail to yield satisfactory results. We propose a traffic flow prediction method named Extended Multi-component External Interactive Gated Recurrent Graph Convolutional Network (EMGRGCN). The extended multi-component (EMC) module is incorporated into the prediction model to address the periodic temporal diffusion problem. Then, we introduce an encoder-decoder architecture that incorporates attention mechanism to capture spatial-temporal dependencies. Specifically, an External Interactive Gated Recurrent Unit (EIGRU) is utilized to capture crucial temporal features. EIGRU and graph convolutional network are combined in the encoder to extract spatial-temporal correlation, and EIGRU and convolutional neural network based decoder transforms the spatial-temporal characteristics into a sequence to predict future traffic flows. Experiments on public transportation datasets PEMSD8 and PEMSD4 demonstrate that EMGRGCN model achieves the best performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Extended Multi-Component Gated Recurrent Graph Convolutional Network for Traffic Flow Prediction


    Contributors:


    Publication date :

    2024-05-01


    Size :

    9639589 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic flow prediction method and system based on multi-time-sequence convolutional gated graph neural network

    SHI QUAN / ZHANG TENGYUN / SHEN QINQIN et al. | European Patent Office | 2023

    Free access

    Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction

    Lv, Mingqi / Hong, Zhaoxiong / Chen, Ling et al. | IEEE | 2021



    Traffic flow prediction model based on gated time convolutional network

    KANG MING | European Patent Office | 2023

    Free access

    TMFO-AGGRU: A Graph Convolutional Gated Recurrent Network for Metro Passenger Flow Forecasting

    Zhang, Yang / Chen, Yanling / Wang, Ziliang et al. | IEEE | 2024