Abstract
The primary challenge in traffic flow prediction centers on effectively capturing the spatio-temporal dependencies within traffic data. To address these challenges, we propose a Spatio-Temporal Feature Fusion Model based on Transformer and a Global Feature Mining Module. The aim is to overcome the high resource consumption issue of the Transformer model when processing large-scale traffic data, as well as its potential shortcomings in capturing subtle spatio-temporal dynamics. The model is capable of precisely capturing the spatio-temporal characteristics of traffic data, achieving seamless integration of temporal and spatial correlations, and revealing the interconnections between global and local features. Through extensive experiments on five real-world traffic datasets, the research results demonstrate a significant improvement in prediction accuracy of our proposed method compared to existing models.
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This work was supported partly by National Natural Science Foundation of China (Nos. 61772249).
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Meng, X., Bai, Y., Li, M., Cai, Z. (2024). Spatio-temporal Fusion of Transformer and Global Feature Mining for Traffic Flow Prediction. In: Huang, DS., Si, Z., Chen, W. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science(), vol 14880. Springer, Singapore. https://doi.org/10.1007/978-981-97-5678-0_13
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DOI: https://doi.org/10.1007/978-981-97-5678-0_13
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