Various fields have recognized the effectiveness of network embedding for handling large-scale graphs. However, accurate traffic conditions prediction using network embedding on large-scale road networks remains challenging due to the intricate correlations in traffic data. To tackle this challenge, we propose a novel framework called Spatio-Temporal Embedding and Attention Mechanism (STEAM). In the embedding process, we design a novel spatial embedding method to consider both the local structure and global structural role of each node. Concurrently, long-term and short-term temporal dependencies are embedded in the temporal part. During the inference process, an attention mechanism is applied to adaptively capture the nonlinear spatio-temporal correlations. Our model was evaluated by predicting traffic speed on two traffic datasets, demonstrating significantly improved prediction performance and superior generalization capabilities.


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    Titel :

    A Spatio-Temporal Embedding and Attention Mechanism for Traffic Prediction in Large-Scale Road Networks


    Beteiligte:
    Zhao, Yaxin (Autor:in) / Xiang, Pengcheng (Autor:in) / Luan, Xinyu (Autor:in) / Liu, Shengjie (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    1372905 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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