Accurate spatial-temporal prediction is a fundamental building block of many real-world applications such as traffic scheduling and management, environment policy making, and public safety. This problem is still challenging due to nonlinear, complicated, and dynamic spatial-temporal dependencies. To address these challenges, we propose a novel embedded spatial-temporal network (ESTNet), which extracts efficient features to model the dynamic correlations and then exploits three-dimension convolution to synchronously model the spatial-temporal dependencies. Specifically, we propose multi-range graph convolution networks for extracting multi-scale static features from the fine-grained road network. Meanwhile, dynamic features are extracted from real-time traffic using a gated recurrent unit network. These features can be applied to identify the dynamic and flexible correlations among sensors and make it possible to exploit a three-dimension convolution unit (3DCon) to simultaneously model the spatial-temporal dependencies. Furthermore, we propose a residual network by stacking multiple 3DCon to capture the nonlinear and complicated dependencies. The effectiveness and superiority of ESTNet are verified on two real-world datasets, and experiments show ESTNet outperforms the state-of-the-art with a significant margin. The code and models will be publicly available.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics


    Beteiligte:
    Luo, Guiyang (Autor:in) / Zhang, Hui (Autor:in) / Yuan, Quan (Autor:in) / Li, Jinglin (Autor:in) / Wang, Fei-Yue (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-10-01


    Format / Umfang :

    14221686 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics

    Jian Feng / Lang Yu / Rui Ma | DOAJ | 2022

    Freier Zugriff

    Multi-Step Spatial-Temporal Fusion Network for Traffic Flow Forecasting*

    Dong, Honghui / Meng, Ziying / Wang, Yiming et al. | IEEE | 2021


    Hierarchical Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network

    Wang, Hanqiu / Zhang, Rongqing / Cheng, Xiang et al. | IEEE | 2022


    Spatial-Temporal Multiscale Fusion Graph Neural Network for Traffic Flow Prediction

    Hou, Hongxin / Ning, Nianwen / Shi, Huaguang et al. | IEEE | 2022


    An effective spatial-temporal attention based neural network for traffic flow prediction

    Do, Loan N.N. / Vu, Hai L. / Vo, Bao Q. et al. | Elsevier | 2019