With the development of modern Intelligent Transportation System (ITS), reliable and efficient transportation information sharing becomes more and more important. Although there are promising wireless communication schemes such as Vehicle-to-Everything (V2X) communication standards, information sharing in ITS still faces challenges such as the V2X communication overload when a large number of vehicles suddenly appeared in one area. This flash crowd situation is mainly due to the uncertainty of traffic especially in the urban areas during traffic rush hours and will significantly increase the V2X communication latency. In order to solve such flash crowd issues, we propose a novel system that can accurately predict the traffic flow and density in the urban area that can be used to avoid the V2X communication flash crowd situation. By combining the existing grid-based and graph-based traffic flow prediction methods, we use a Topological Graph Convolutional Network (ToGCN) followed with a Sequence-to-sequence (Seq2Seq) framework to predict future traffic flow and density with temporal correlations. The experimentation on a real-world taxi trajectory traffic data set is performed and the evaluation results prove the effectiveness of our method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction


    Contributors:
    Qiu, Han (author) / Zheng, Qinkai (author) / Msahli, Mounira (author) / Memmi, Gerard (author) / Qiu, Meikang (author) / Lu, Jialiang (author)


    Publication date :

    2021-07-01


    Size :

    2921198 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Urban traffic flow space-time prediction scheme based on graph convolutional neural network

    ZHANG RONGQING / WANG HANQIU / LI BING | European Patent Office | 2021

    Free access

    Traffic flow prediction method based on graph convolutional network

    XU HUI / MENG FANYU / REN QIANQIAN et al. | European Patent Office | 2025

    Free access

    Urban traffic flow prediction method based on graph convolutional network and related equipment

    LONG WANGCHEN / LIN JIA / YIN XUEMEI et al. | European Patent Office | 2025

    Free access

    Graph Attention Convolutional Network: Spatiotemporal Modeling for Urban Traffic Prediction

    Song, Qingyu / Ming, RuiBo / Hu, Jianming et al. | IEEE | 2020


    Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction

    Lv, Mingqi / Hong, Zhaoxiong / Chen, Ling et al. | IEEE | 2021