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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    Title :

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


    Contributors:
    Liu, Tao (author) / Jiang, Aimin (author) / Zhou, Jia (author) / Li, Min (author) / Kwan, Hon Keung (author)

    Published in:

    Publication date :

    2023-10-01


    Size :

    2159098 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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