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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    Title :

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


    Contributors:
    Huang, Jing (author) / Luo, Kun (author) / Cao, Longbing (author) / Wen, Yuanqiao (author) / Zhong, Shuyuan (author)

    Published in:

    Publication date :

    2022-11-01


    Size :

    2702081 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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