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Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network

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Abstract

Traffic forecasting using deep learning represents a crucial aspect of intelligent transportation systems, carrying substantial implications for congestion reduction and efficient route planning. Despite its significance, accurately predicting traffic states remains a challenge. Existing methodologies focus on capturing the temporal trends of traffic states and the spatial dependencies between roads to enhance prediction accuracy. However, two noteworthy limitations persist in these approaches: (1) Many models neglect the interaction between spatiotemporal features across varying time spans, hindering their ability to utilize traffic state information effectively for predicting future conditions. (2) Genuine correlations between roads are time-varying, making it inadequate to rely on static graphs or static pre-trained node embeddings to model dynamic correlations between roads. To address these challenges, we propose the Multiple Time-Scale Graph Attention Network (MTS-GATN), which comprises two key modules: the Multiple Time-Scale Spatiotemporal Features Extraction Module and the Feature Augmentation Module. The first module involves stacking multiple spatiotemporal extraction layers to discern traffic state information at different time scales. In the second module, we employ dynamic spatial semantic embedding for feature augmentation, providing nodes with dynamic representations over time. Subsequently, we leverage a multi-head spatiotemporal attention mechanism to comprehensively consider location information and real-time semantic data, facilitating the interaction of traffic state information across multiple time scales. Experimental results on two distinct traffic datasets validate the superior performance of MTS-GATN in medium-term and long-term forecasting scenarios.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China(grant entitled “Connected vehicle big data driven expressway multi-objective coordinated control fusing deep learning and traffic flow model”, award number 466 71901070).

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Authors and Affiliations

  1. College of Civil Engineering, Fuzhou University, Fuzhou, China

    Jie Fang, Zhichao Wu, Mengyun Xu & Hongting Chen

  2. The Intelligent Transport System Research Center, Wuhan University of Technology, 1178 Heping Road, Wuhan, Hubei, 430063, China

    Mengyun Xu

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  1. Jie Fang
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Correspondence to Mengyun Xu.

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Fang, J., Wu, Z., Xu, M. et al. Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network. Appl Intell 54, 7479–7492 (2024). https://doi.org/10.1007/s10489-024-05503-0

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