The graph-based traffic forecasting is generally realized on the assumption of sufficient data, which could be impractical in the regions without well-deployed mobile sensors or data-processing facilities. Recent studies have developed a solution with the cross-region transfer learning, i.e. transferring traffic knowledge from the source regions to target ones, whose traffic data and computing resources are limited. Nevertheless, relevant issues, including initialization selection and domain adaptation, have not been effectively tackled in the cross-region graph-based traffic forecasting. This paper proposes the cluster-granularity spatiotemporal transfer (CGSTT), which transfers the cluster-granularity knowledge from the source region to target one for the cross-region graph-based traffic forecasting, as not all source knowledge is positive to the target region. Additionally, the domain adaptation is achieved by the dual alignment consisting of the covariate alignment and label alignment of the source/target data, making the proposed CGSTT adapt to the target region efficiently. The superiority of the proposed method over ten compared baseline methods for both short-term and long-term predictions is demonstrated by the conducted experiments on four tasks, which show that it outperforms the state-of-the-art method by achieving an 8.89% average improvement in forecasting accuracy. The PyTorch implementation of the CGSTT is available at https://github.com/canyangguo/CGSTT.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Cluster-Granularity Spatiotemporal Transfer for Cross-Region Graph-Based Traffic Forecasting


    Contributors:


    Publication date :

    2025-07-01


    Size :

    4590733 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    GSTGAT: Gated spatiotemporal graph attention network for traffic demand forecasting

    Shuilin Yao / Huizhen Zhang / Chenxi Wang et al. | DOAJ | 2024

    Free access

    GSTGAT: Gated spatiotemporal graph attention network for traffic demand forecasting

    Yao, Shuilin / Zhang, Huizhen / Wang, Chenxi et al. | Wiley | 2024

    Free access

    Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

    Xu, Yaobin / Liu, Weitang / Mao, Tingyun et al. | Transportation Research Record | 2022


    Spatiotemporal Graph Neural Networks for Traffic Forecasting: A Comparative Analysis

    Rao, Komati Venkateswara / Selvakumar, R. K. | Springer Verlag | 2024