Deep learning algorithms are considered as the best-fit methods to deal with spatial-temporal attributes of short-term traffic predictions in recent years. Further, the graph-based Graph Convolutional Network (GCN) models are widely used to handle the spatial dependence of roads in urban networks. This paper aims to explore the spatial graph representation of urban networks for GCN models. Specifically, a data-driven spatial graph representation scheme is established to measure the complex non-linear relationships among roads, together with the local and non-local impacts of urban traffics. This spatial graph representation is then combined with Sequence to Sequence structure to present a multi-input and multi-output network-wide traffic prediction model (SGDE-S2S model). A sensitive test is carried out to select the optimal threshold value of the most relevant roads to every target road. Then the SGDE-S2S model and some other baseline models are tested on real-world traffic speed data of Chengdu, China. The experiment results confirm that the SGDE-S2S model can well capture the intrinsic relationships of roads without the need for topological adjacent information and performs the best in all multi-step predictions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Mining the Graph Representation of Traffic Speed Data for Graph Convolutional Neural Network


    Contributors:
    Mao, Jiannan (author) / Huang, Hao (author) / Chen, Yuting (author) / Lu, Weike (author) / Chen, Guoqiang (author) / Liu, Lan (author)


    Publication date :

    2021-09-19


    Size :

    1228747 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Spatial–Temporal Tensor Graph Convolutional Network for Traffic Speed Prediction

    Xu, Xuran / Zhang, Tong / Xu, Chunyan et al. | IEEE | 2023


    Traffic jam prediction method based on tense graph convolutional neural network

    ZHANG HAO / ZHANG GE / HUA QIFAN et al. | European Patent Office | 2023

    Free access


    Graph attention temporal convolutional network for traffic speed forecasting on road networks

    Zhang, Ke / He, Fang / Zhang, Zhengchao et al. | Taylor & Francis Verlag | 2021