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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    Title :

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


    Contributors:


    Publication date :

    2024-09-24


    Size :

    1372905 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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