Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing such dependencies is critical to improving prediction accuracy. Recently, many deep learning models have been proposed for spatial-temporal dependency modeling. While numerous deep learning models have been developed for spatial-temporal dependency modeling, most rely on different types of convolutions to extract spatial and temporal correlations separately. To address this limitation, we propose a novel deep learning framework for traffic prediction called GraphSAGE-based Dynamic Spatial-Temporal Graph Convolutional Network (DST-GraphSAGE), which can capture dynamic spatial and temporal dependencies simultaneously. Our model utilizes a spatial-temporal GraphSAGE module to extract localized spatial-temporal correlations from past observations of a node’s spatial neighbors. Meanwhile, the attention mechanism is incorporated to dynamically learn weights between traffic nodes based on graph features. Additionally, to capture long-term trends in traffic data, we employ dilated causal convolution as the temporal convolution layer. A series of numerical experiments are conducted on five real-world datasets, which demonstrates the effectiveness of our model for spatial-temporal dependency modeling.


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    Titel :

    GraphSAGE-Based Dynamic Spatial–Temporal Graph Convolutional Network for Traffic Prediction


    Beteiligte:
    Liu, Tao (Autor:in) / Jiang, Aimin (Autor:in) / Zhou, Jia (Autor:in) / Li, Min (Autor:in) / Kwan, Hon Keung (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-10-01


    Format / Umfang :

    2159098 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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