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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Zhang, Tong (author) / Jin, Junchen (author) / Yang, Hui (author) / Guo, Haifeng (author) / Ma, Xiaoliang (author)


    Publication date :

    2019-10-01


    Size :

    923712 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Real-time traffic conflict prediction at signalized intersections using vehicle trajectory data and deep learning

    Zhang, Gongquan / Jin, Jieling / Chang, Fangrong et al. | Elsevier | 2024

    Free access

    Urban traffic assignment based on signalized intersection delays

    V.D. Minh / Horbachov Peter / Kolii Oleksandr et al. | DOAJ | 2020

    Free access

    Optimization of Signalized Traffic Network using Swarm Intelligence

    Tan, Min Keng / Ladillah, Mohd.Riezman / Chuo, Helen Sin Ee et al. | IEEE | 2021