Traffic forecasting aims to capture complex spatial-temporal dependencies and non-linear dynamics, which plays an indispensable role in intelligent transportation systems and other domains like neuroscience, climate, etc. Most recent works rely on graph convolutional networks (GCN) to model the dependencies and the dynamics. However, the over-smoothing issue of GCN would produce indistinguishable features among nodes, leading to poor expressivity and weak capability of modeling complex dependencies and dynamics. To address this issue, we present a novel clustering spatial-temporal (ClusterST) unit, which incorporates unsupervised learning into GCN for extracting discriminative features. Specifically, we first exploit a neural network to learn a dynamic clustering, i.e., learning to partition the neighbors of each node into clusters at each time step. Two probabilistic losses are proposed to improve the separability of clusters. Then, the extracted features of different clusters can be distinguished. Based on the dynamically formed clusters, a vanilla GCN is applied to aggregate features within each cluster. By purely exploiting such a ClusterST unit, large improvements over the state-of-the-art are achieved. Furthermore, ClusterST units with a different number of clusters can be regarded as basic components to construct an inception-like ClusterST network for going deeper. We evaluate the framework on two real-world large-scale traffic datasets and observe an average improvement of 18.19% and 7.62% over state-of-the-art baselines, respectively. The code and models will be publicly available.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    ClusterST: Clustering Spatial–Temporal Network for Traffic Forecasting


    Contributors:
    Luo, Guiyang (author) / Zhang, Hui (author) / Yuan, Quan (author) / Li, Jinglin (author) / Wang, Fei-Yue (author)


    Publication date :

    2023-01-01


    Size :

    20228740 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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

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



    Spatial‐temporal correlation graph convolutional networks for traffic forecasting

    Ru Huang / Zijian Chen / Guangtao Zhai et al. | DOAJ | 2023

    Free access

    Dynamic Spatial–Temporal Convolutional Networks for Traffic Flow Forecasting

    Zhang, Hong / Kan, Sunan / Zhang, XiJun et al. | Transportation Research Record | 2023