Temporal traffic prediction is critical for ITS yet remains challenging in handling complex spatio-temporal dynamics of traffic systems. The continuous traffic data (e.g., traffic flow, and speed) from various channels and nodes in a traffic network are coupled with each other over the time points of each channel, spatially between traffic nodes, and jointly in both spatial and temporal dimensions. Such multi-aspect traffic data couplings reflect the conditions of a real-life traffic system and evolve over traffic movement and network dynamics. The recent studies formulate traffic prediction by high-profile graph neural networks. However, they mainly focus on hidden relations captured by neural graph mechanisms while overlooking or simplifying the above multi-aspect traffic data couplings. By modeling a traffic system as a coupled traffic network, we learn the multi-aspect traffic data couplings by a Multi-relational Synchronous Graph Attention Network (MS-GAT). Specifically, MS-GAT learns three embeddings to respectively but synchronously represent the traffic data-based channel, temporal, and spatial relations between nodes by specific graph attention designs. The embeddings are further adaptively coupled according to their respective importance to prediction. Tested on five real-world datasets, MS-GAT outperforms six SOTA graph networks-based traffic predictors. MS-GAT captures not only spatial and temporal couplings but also traffic data-based channel interactions over traffic evolution.


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    Titel :

    Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction


    Beteiligte:
    Huang, Jing (Autor:in) / Luo, Kun (Autor:in) / Cao, Longbing (Autor:in) / Wen, Yuanqiao (Autor:in) / Zhong, Shuyuan (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2022


    Format / Umfang :

    2702081 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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