Predicting traffic speed is of importance in transportation management. Signalized road networks manifest highly dynamic speed patterns that are challenging to model and predict. We propose a hybrid deep-learning-based approach for link speed prediction, aiming at capturing heterogeneous spatiotemporal correlations between road intersections. After transforming original road networks and intersections into graphs, this approach leverages a layered graph convolution network structure to model traffic speed variations at both intersection and road network levels. The two levels are combined through a fully connected neural layer. Neural spatiotemporal attention mechanisms are applied to modulate the most relevant periodical traffic information during signal cycles. The proposed approach was evaluated using real-world speed data collected in Hangzhou City, China. Experiments demonstrate that the proposed approach can offer a scalable and effective solution for predicting short-term speed for signalized road networks.


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    Titel :

    Link speed prediction for signalized urban traffic network using a hybrid deep learning approach


    Beteiligte:
    Zhang, Tong (Autor:in) / Jin, Junchen (Autor:in) / Yang, Hui (Autor:in) / Guo, Haifeng (Autor:in) / Ma, Xiaoliang (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    923712 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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