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Smart city article details
| Title | Mra-Dgcn: Multi-Range Attention-Based Dynamic Graph Convolutional Network For Traffic Prediction |
|---|---|
| ID_Doc | 38024 |
| Authors | Yao H.; Chen R.; Xie Z.; Yang J.; Hu M.; Guo J. |
| Year | 2022 |
| Published | Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 |
| DOI | http://dx.doi.org/10.1109/BigData55660.2022.10020493 |
| Abstract | Accurately obtaining information of road traffic conditions is of great significance to people's travel planning and arrangement of social shared resources, and has become a major research focus in the field of smart cities. Accurately predicting road conditions poses a huge challenge due to the complex spatial correlations and nonlinear temporal dependencies of real-time traffic networks. In this paper we propose a Multi-Range Attention-Based Dynamic Graph Convolutional Network (MRA-DGCN) to model complex traffic networks. The MRA-DGCN model uses a bicomponent modules to separate different periodicity to extract refined traffic signal. In the MRA-DGCN model, we use the adaptive spatial-temporal network block (ASTnet block), which includes dynamic graph convolution and temporal attention, to mine complex spatial correlations and nonlinear temporal dependencies, respectively. In the adaptive spatial-temporal network block, we use dynamically generated adjacency matrices instead of existing distance-based adjacency matrices to perform graph convolution operations to aggregate information between nodes during model training. Instead of hierarchically extracting spatial-temporal signal, we adopt temporal attention to capture the spatial-temporal information synchronously to improve the prediction performance. Furthermore, we propose a residual gated network to control the flow of information passed to the next hidden layer to enhance the predictive accuracy. Extensive experiments on two real-world traffic datasets, METR-LA and PeMS-BAY, show that the MRA-DGCN achieves the state-of-the-art results. © 2022 IEEE. |
| Author Keywords | attention; graph neutral network; spatial-temporal correlation; traffic prediction |
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