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.


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    Titel :

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


    Beteiligte:
    Zhao, Junhui (Autor:in) / Xiong, Xincheng (Autor:in) / Zhang, Qingmiao (Autor:in) / Wang, Dongming (Autor:in)


    Erscheinungsdatum :

    2024-05-01


    Format / Umfang :

    9639589 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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