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Smart city article details

Title	Spatio-Temporal Graph-Tcn Neural Network For Traffic Flow Prediction
ID_Doc	52568
Authors	Ren H.; Kang J.; Zhang K.
Year	2022
Published	2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2022
DOI	http://dx.doi.org/10.1109/ICCWAMTIP56608.2022.10016530
Abstract	Building smart cities in the new era depend heavily on traffic flow analysis, forecast, and management. How to integrate time series and spatial data is a crucial difficulty for anticipating traffic patterns in a smart city. An evident flaw in the existing GCN-based approach is that it is unable to collect non-adjacent but related spatial information because the adjacency matrix only contains the original topological spatial information. In this work, we create a brand-new kind of adjacency matrix that includes both prospective spatial relationships and unique spatial properties using a cutting-edge data-driven methodology. Furthermore, we develop a high-accuracy Spatio-Temporal Graph-TCN Neural Network, called ST-GTNN, for traffic flow prediction. The graph spatial attention layer and the channel attention layer are specifically used to be aware of spatial features, whereas the TCN layer and the temporal attention mechanism are used to fit temporal interactions. Experiment results on two real datasets show that our proposed ST-GTNN outperforms existing methods in terms of prediction accuracy. © 2022 IEEE.
Author Keywords	Deep learning; Graph convolution network; Spatio-temporal modeling; Traffic flow prediction

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