Highlights Bayesian inference and deep learning is integrated for traffic congestion modeling; A dynamic Bayesian graph convolutional network (DBGCN) is proposed. The DBGCN outperforms the state-of-the-art prediction models. The DBGCN can simulate congestion evolution via dynamic adjacency matrix. The change of congestion source location leads to different congestion patterns.

    Abstract Congestion, whether recurrent or non-recurrent, propagates through the road network. The process of congestion propagation from a particular road to its neighbors can be regarded as a kind of message passing with a directed relationship. Existing methods have created a solid foundation for characterizing congestion propagation; however, they are either built upon simplified assumptions in traffic flow theory or predefined relationships among road sections, which would lead to downgraded accuracy in practice. This paper proposes a dynamic Bayesian graph convolutional network (DBGCN), which integrates Bayesian inference into a deep learning framework. Therefore, the rules of congestion propagation in the network can be actively learned from the observed data instead of predefining them based on prior knowledge. Experimental results on 971 testbeds in a regional road network in Beijing demonstrate that DBGCN outperforms the state-of-the-art models in inferring the congestion propagation spatiotemporal coverage and reveals variations in congestion propagation patterns according to the road network structure. Furthermore, the proposed model can simulate the congestion propagation process in customized scenarios by learning the latent congestion propagation rules. The results in different scenarios show that the change of congestion source location leads to distinct congestion magnitude, and the propagation of congestion will eventually stop at the road sections with strong shunting effect.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic congestion propagation inference using dynamic Bayesian graph convolution network


    Beteiligte:
    Luan, Sen (Autor:in) / Ke, Ruimin (Autor:in) / Huang, Zhou (Autor:in) / Ma, Xiaolei (Autor:in)


    Erscheinungsdatum :

    2021-12-13




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction

    Sun, Yanfeng / Jiang, Xiangheng / Hu, Yongli et al. | IEEE | 2022


    Dynamic graph convolution traffic speed prediction method

    LIU QILIANG / YUAN HAOTAO / YANG LIU et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    Traffic prediction method based on dynamic graph convolution

    FAN JIN / WENG WENCHAO / TIAN HAO et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Learning Traffic Network Embeddings for Predicting Congestion Propagation

    Sun, Yidan / Jiang, Guiyuan / Lam, Siew-Kei et al. | IEEE | 2022


    Graph transformer based dynamic multiple graph convolution networks for traffic flow forecasting

    Yongli Hu / Ting Peng / Kan Guo et al. | DOAJ | 2023

    Freier Zugriff