Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are introduced into traffic prediction and achieve state-of-the-art performance due to their good ability for modeling the spatial and temporal property of traffic data. In spite of having good performance, the current methods generally focus on the traffic measurement of road segments, i.e. the nodes of traffic flow graph, while the edges of the graph, which represent the correlation of traffic data of different road segments and form the affinity matrix for GCN, are usually constructed according to the structure of road network, but the spatial and temporal properties are not well exploited in their theories. In this paper, we propose a Dual Dynamic Spatial-Temporal Graph Convolution Network (DDSTGCN), which not only models the dynamic property of the nodes of the traffic flow graph but also captures the dynamic spatial-temporal feature of the edges by transforming the traffic flow graph into its dual hypergraph. The traffic prediction is enhanced by the collaborative convolutions on the traffic flow graph and its dual hypergraph. The proposed method is evaluated by extensive traffic prediction experiments on six real road datasets and the results show that it outperforms state-of-the-art related methods. Source codes are available at https://github.com/j1o2h3n/DDSTGCN.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction


    Contributors:
    Sun, Yanfeng (author) / Jiang, Xiangheng (author) / Hu, Yongli (author) / Duan, Fuqing (author) / Guo, Kan (author) / Wang, Boyue (author) / Gao, Junbin (author) / Yin, Baocai (author)

    Published in:

    Publication date :

    2022-12-01


    Size :

    3720276 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Dynamic graph convolution traffic speed prediction method

    LIU QILIANG / YUAN HAOTAO / YANG LIU et al. | European Patent Office | 2020

    Free access

    Traffic Flow Prediction Using Novel Spatial-Temporal Multi-Head Attention Graph Convolution Networks

    Cheng, Yuan / Peng, Cheng / Wang, Ze et al. | Transportation Research Record | 2024