The transportation system is a complex dynamic giant system which integrates and intertwines the elements of people, vehicles, roads, and the environment. The city-level traffic flow forecasting can effectively reflect the flow changes of the traffic system and provide practical guidance for the formulation of traffic rules. Recent city-level traffic flow forecasting works rely on accurate prior knowledge of graphs (i.e., the spatial relationships between roads), which hinders their effectiveness and application in the real world. We propose a novel framework for urban traffic flow forecasting, which simultaneously infers and utilizes the relationship between time series. In our model, the graph structure learning module dynamically captures the correlation and causation between the different time series and infers a potentially fully connected graph. At the same time, the temporal convolution network captures the temporal correlation between a single time series. The graph neural network uses the graph for forecasting. Our model no longer relies on accurate graph priors and achieves better forecasting results than previous work. Experiments on two public datasets verify that the proposed model is very competitive.


    Access

    Download


    Export, share and cite



    Title :

    Urban Traffic Flow Forecasting Based on Graph Structure Learning


    Contributors:
    Guangyu Huo (author) / Yong Zhang (author) / Yimei Lv (author) / Hao Ren (author) / Baocai Yin (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Deep Learning Mechanism for Region Based Urban Traffic Flow Forecasting

    Bansal, Nishu / Bali, Rasmeet Singh | Springer Verlag | 2023


    Contrastive learning for traffic flow forecasting based on multi graph convolution network

    Kan Guo / Daxin Tian / Yongli Hu et al. | DOAJ | 2024

    Free access

    Contrastive learning for traffic flow forecasting based on multi graph convolution network

    Guo, Kan / Tian, Daxin / Hu, Yongli et al. | Wiley | 2024

    Free access


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

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

    Free access