Abstract
With rapid economic development, the per capita ownership of automobiles in our country has begun to rise year by year. More researchers have paid attention to using scientific methods to solve traffic flow problems. Traffic flow prediction is not simply affected by the number of vehicles, but also contains various complex factors, such as time, road conditions, and people flow. However, the existing methods ignore the complexity of road conditions and the correlation between individual nodes, which leads to the poor performance. In this study, a deep learning model SAMGCN is proposed to effectively capture the correlation between individual nodes to improve the performance of traffic flow prediction. First, the theory of spatiotemporal decoupling is used to divide each time of each node into finer particles. Second, multimodule fusion is used to mine the potential periodic relationships in the data. Finally, GRU is used to obtain the potential time relationship of the three modules. Extensive experiments were conducted on two traffic flow datasets, PeMS04 and PeMS08 in the Caltrans Performance Measurement System to prove the validity of the proposed model.
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Acknowledgment
This work was supported by the National Key R&D Program of China under Grant No. 2020YFB1710200, and the National Natural Science Foundation of China under Grant No. 61872105 and No. 62072136.
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Li, L., Shao, H., Chen, J., Wang, Y. (2022). Self-attention Based Multimodule Fusion Graph Convolution Network for Traffic Flow Prediction. In: Wang, Y., Zhu, G., Han, Q., Wang, H., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1628. Springer, Singapore. https://doi.org/10.1007/978-981-19-5194-7_1
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