In recent years, more and more scholars have applied graph neural networks in combination with other modules to the field of traffic flow forecasting and achieved outstanding results. Most of these graph-based methods describe pairwise relationships between two objects, but in real transportation networks, the relationships between objects are often of a high order. To effectively learn the higher-order relationships between objects, this paper proposes a dynamic spatio-temporal residual hypergraph convolutional network for traffic forecasting (Res-DSTHGCN). In this paper, we combine dynamic spatio-temporal graph convolution and dynamic spatio-temporal residual hypergraph convolution to capture more global spatio-temporal features than models with only spatio-temporal graph convolution or only spatio-temporal hypergraph convolution. Meanwhile, the hypergraph convolutional network enhanced by residual connectivity improves the oversmoothing problem that may occur in the traditional hypergraph convolutional network (HGCN) along with the continuous stacking of layers. It is proved that the prediction accuracy of the method proposed in this paper is improved by conducting experiments for comparison with other baselines.


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    Title :

    Dynamic Spatio-Temporal Residual Hypergraph Convolutional Networks for Traffic Flow Forecasting


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Su, Jun (author) / Wang, Hairu (author) / Przystupa, Krzysztof (author) / Kochan, Orest (author) / Liu, Donghua (author)


    Publication date :

    2025-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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