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Spatiotemporal fuzzy-graph convolutional network model with dynamic feature encoding for traffic forecasting
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DOI：10.1016/j.knosys.2021.107403.png)
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Accurate traffic forecasting is challenging, owing to the complex spatial dependence of traffic networks and the dynamic time dependence of traffic patterns. In this study, a novel spatiotemporal fuzzy-graph convolutional network model with dynamic feature encoding is proposed to realize accurate traffic forecasting. The proposed model combines graph convolutional network and long short-term memory network to extract complicated spatiotemporal dependence features of traffic data. Furthermore, a new graph generation method based on the fuzzy C-means clustering is designed to enhance the representation ability of the spatial dependencies between stations in a traffic network. Moreover, to make the graph convolutional network fully consider both global and local spatiotemporal dependency relationship between the stations in the process of convolution operation, a new node feature construction method is proposed. Finally, the forecasting performance of the proposed model is verified on three real-world traffic datasets. The experimental results demonstrate that the proposed model outperforms other baseline models in terms of both spatiotemporal feature extraction and long short-term forecasting. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Traffic forecasting
Graph convolutional networks
Fuzzy C-means
Long short-term memory network
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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