Abstract
Traffic flow prediction is one of the core issues in the field of transportation planning and management. Traditional traffic flow prediction methods are limited by factors such as data sparsity, long-term interdependencies, and intricate spatiotemporal dynamics. To overcome these challenges, this paper proposes a novel predictive model called ARSTGCN, which incorporates spatiotemporal attention mechanisms and deep learning networks. Firstly, the spatiotemporal attention mechanism assigns attention weights to traffic sensor nodes, enabling the capture of spatiotemporal relationships. Dilation convolution is employed to process the temporal correlation in the data, mitigating concerns of gradient explosion and vanishing during the training of lengthy time series. Secondly, data containing spatiotemporal weight features undergoes input into the graph convolutional network, facilitating the capture of spatial dynamic correlations. The final prediction results are obtained through the utilization of the fully connected layer. Compared with the baseline model on two publicly available datasets, ARSTGCN showed certain advantages.
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Li, S., Li, J., Lan, J., Lu, Y. (2023). Traffic Flow Prediction Based on Attention Mechanism Convolutional Neural Network. In: Yang, Y., Wang, X., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2023 . AIMS 2023. Lecture Notes in Computer Science, vol 14202. Springer, Cham. https://doi.org/10.1007/978-3-031-45140-9_5
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