An Attention and Wavelet Based Spatial-Temporal Graph Neural Network for Traffic Flow and Speed Prediction

Shihao Zhao, Shuli Xing and Guojun Mao ()
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Shihao Zhao: School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
Shuli Xing: School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
Guojun Mao: School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China

Mathematics, 2022, vol. 10, issue 19, 1-15

Abstract: Traffic flow prediction is essential to the intelligent transportation system (ITS). However, due to the complex spatial-temporal dependence of traffic flow data, it is insufficient in the extraction of local and global spatial-temporal correlations for the previous process on road network and traffic flow modeling. This paper proposes an attention and wavelet-based spatial-temporal graph neural network for traffic flow and speed prediction (STAGWNN). It integrated attention and graph wavelet neural networks to capture local and global spatial information. Meanwhile, we stacked a gated temporal convolutional network (gated TCN) with a temporal attention mechanism to extract the time series information. The experiment was carried out on real public transportation datasets: PEMS-BAY and PEMSD7(M). The comparison results showed that our proposed model outperformed baseline networks on these datasets, which indicated that STAGWNN could better capture the spatial-temporal correlation information.

Keywords: wavelet transform; graph convolutional network; attention mechanism; intelligent transportation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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