Abstract
Due to the particularity of “Tourist chartered Buses, Liner Buses and Dangerous Goods Transport Vehicles” (“TLD Vehicles”), traffic accidents will bring serious losses. Therefore, traffic flow prediction for “TLD Vehicles” has become an urgent need for traffic management departments. Different from the ordinary traffic flow prediction problem, the traffic flow for “TLD Vehicles” has the characteristics of sparsity in the spatial dimension. The ordinary spatial feature extraction method will capture useless node information and affect the prediction accuracy. The traffic data of “TLD Vehicles” has significant periodic characteristics in the time dimension. Most of the traditional traffic prediction methods extract time characteristics through artificially set cycles, which has certain limitations. In this paper, a Period Extraction model for Traffic Flow Prediction is proposed to solve the problem of data sparsity and insufficient periodic feature extraction ability of traffic flow prediction. The model uses sparse graph convolution combined with Transformer to extract spatial features, uses sequence decomposition and auto-correlation attention mechanism to extract temporal features, and obtains prediction results through stacked spatio-temporal modules. The experimental results show that the proposed algorithm can extract the traffic f low data feature information with stable characteristics more effectively and improve the accuracy of the network model for traffic prediction.
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Acknowledgements
This work was supported in part by the Hebei Province Innovation Capability Enhancement Plan Project under Grant 22567603H, in part by S&T Program of Hebei under Grant 20310801D, in part by research project (the fourth batch) of KT12 section of the new line of Rongwu Expressway.
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Wang, Q. et al. (2024). Period Extraction for Traffic Flow Prediction. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14489. Springer, Singapore. https://doi.org/10.1007/978-981-97-0798-0_29
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DOI: https://doi.org/10.1007/978-981-97-0798-0_29
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