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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    Title :

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


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

    Published in:

    Publication date :

    2023-12-01


    Size :

    3693381 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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