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MSSTN: a multi-scale spatio-temporal network for traffic flow prediction.
- Source :
- International Journal of Machine Learning & Cybernetics; Jul2024, Vol. 15 Issue 7, p2827-2841, 15p
- Publication Year :
- 2024
-
Abstract
- Spatio-temporal feature extraction and fusion are crucial to traffic prediction accuracy. However, the complicated spatio-temporal correlations and dependencies between traffic nodes make the problem quite challenging. In this paper, a multi-scale spatio-temporal network (MSSTN) is proposed to exploit complicated local and nonlocal correlations in traffic flow for traffic prediction. In the proposed method, a convolutional neural network, a self-attention module, and a graph convolution network (GCN) are integrated to extract and fuse multi-scale temporal and spatial features to make predictions. Specifically, a self-adaption temporal convolutional neural network (SATCN) is first employed to extract local temporal correlations between adjacent time slices. Furthermore, a self-attention module is applied to capture the long-range nonlocal traffic dependence in the temporal dimension and fuse it with the local features. Then, a graph convolutional network module is utilized to learn spatio-temporal features of the traffic flow to exploit the mutual dependencies between traffic nodes. Experimental results on public traffic datasets demonstrate the superiority of our method over compared state-of-the-art methods. The ablation experiments confirm the effectiveness of each component of the proposed model. Our implementation on Pytorch is publicly available at https://github.com/csust-sonie/MSSTN. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18688071
- Volume :
- 15
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- International Journal of Machine Learning & Cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 177896041
- Full Text :
- https://doi.org/10.1007/s13042-023-02067-2