Traffic flow forecasting on a large-scale sensor network is of great practical significance for policy decision-making, urban management, and transport planning. Recently, several prediction methods based on graph convolution have been put forward. However, they are limited to small-scale analyses because of high computation complexity and fail to integrate spatial-temporal dependencies sufficiently between sensors with a large topological distance in multiple time steps. To address these issues, we propose a novel deep framework called PFNet to perform large-scale traffic forecasting. PFNet captures temporal correlations using deep multi-view sequence encoders (DMVSE) and spatial correlations using graph embedding technologies on transportation networks. Spatial-temporal dependencies are more comprehensively fused using cascaded progressive attention (CPA) modules due to the full use of latent temporal and spatial representation. Experiments are conducted on two large-scale traffic datasets (LondonHW and ManchesterHW) from England Highway. The results demonstrate that PFNet outperforms several existing state-of-the-art approaches.


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    Titel :

    PFNet: Large-Scale Traffic Forecasting With Progressive Spatio-Temporal Fusion


    Beteiligte:
    Wang, Chen (Autor:in) / Zuo, Kaizhong (Autor:in) / Zhang, Shaokun (Autor:in) / Lei, Hanwen (Autor:in) / Hu, Peng (Autor:in) / Shen, Zhangyi (Autor:in) / Wang, Rui (Autor:in) / Zhao, Peize (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-12-01


    Format / Umfang :

    3693381 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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