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Long-term traffic forecasting based on adaptive graph cross strided convolution network

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Abstract

Traffic forecasting aims to use historical information to predict future traffic values, to achieve the purpose of easing traffic pressure. However, most existing methods can not extract spatial-temporal features from historical data comprehensively and maintain high-accuracy forecasting in long-term forecasting continuously. In this paper, we design a adaptive graph cross strided convolution network (AGCSCN) for long-term traffic forecasting, which mainly includes two deep learning components: crossd stride convolution network (CSCN) for temporal features extraction and adaptive graph convolution network (AGCN) for spatial features extraction. CSCN component ensures that all the historical information can be perceived, and uses parallel cross convolution kernels to enhance long-term forecasting ability by reflecting the difference over forecasting horizons. AGCN component further learns the spatial correlation of period and trend segments respectively on the basis of global adaptive spatial features perception. The experimental results on four real-world traffic datasets show that the proposed AGCSCN model outperforms the state-of-art baselines and achieves optimal forecasting accuracy over all forecasting horizons.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No.61971057 and MoE-CMCC “Artifical Intelligence” under Project No.MCM20190701.

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Authors and Affiliations

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Zhao Li, Yong Zhang, Da Guo & Xu Zhou

  2. Bejing Key Laboratory of Work Safety Intelligent Monitoring, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yong Zhang & Da Guo

  3. China Mobile Research Institue, Beijing, 100053, China

    Xing Wang & Lin Zhu

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  1. Zhao Li
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  2. Yong Zhang
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  3. Da Guo
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  4. Xu Zhou
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  6. Lin Zhu
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Correspondence to Yong Zhang.

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Li, Z., Zhang, Y., Guo, D. et al. Long-term traffic forecasting based on adaptive graph cross strided convolution network. Appl Intell 53, 3672–3686 (2023). https://doi.org/10.1007/s10489-022-03739-2

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