Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation
| Autor: | Baocai Yin, Zhen (Sean) Qian, Junbin Gao, Yongli Hu, Kan Guo, Yanfeng Sun |
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| Rok vydání: | 2022 |
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| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 23:1009-1018 |
| ISSN: | 1558-0016 1524-9050 |
| DOI: | 10.1109/tits.2020.3019497 |
| Popis: | Traffic forecasting is a challenging problem in the transportation research field as the complexity and non-stationary changing of the traffic data, thus the key to the issue is how to explore proper spatial and temporal characteristics. Based on this thought, many creative methods have been proposed, in which Graph Convolution Network (GCN) based methods have shown promising performance. However, these methods depend on the graph construction, which mainly uses the prior knowledge of the road network. Recently, some works realized the fact of the road network graph changing and tried to construct dynamic graphs for GCN, but they do not fully exploit the spatial and temporal properties of the traffic data in the graph construction. In this paper, we propose a novel dynamic graph convolution network for traffic forecasting, in which a latent network is introduced to extract spatial-temporal features for constructing the dynamic road network graph matrices adaptively. The proposed method is evaluated on several traffic datasets and the experimental results show that it outperforms the state of the art traffic forecasting methods. The website of the code is https://github.com/guokan987/DGCN.git. |
| Databáze: | OpenAIRE |
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