- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | XIA Ying, LIU Min. Traffic Flow Prediction Based on Spatial-Temporal Attention Convolutional Neural Network[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 340-347. doi: 10.3969/j.issn.0258-2724.20210526
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In order to fully exploit the complex spatial-temporal dynamic correlation of traffic flow and improve the accuracy of traffic flow prediction, a spatial attention mechanism and an dilated causal convolutional neural network are introduced. A traffic flow prediction model STACNN based on spatial-temporal attention convolutional neural network is proposed. Firstly, the gated temporal convolution network block constructed by dilated causal convolution and gating unit is used to obtain the nonlinear temporal dynamic correlation of traffic flow and avoid gradient disappearance or gradient explosion when training long-term sequences. Secondly, the spatial attention mechanism is used to automatically assign attention weights to the traffic sensor nodes in the road network, which can dynamically pay attention to the spatial relationship between non-adjacent nodes, and combine the graph convolutional neural network to extract the local spatial dynamic correlation of the road network. Then, the final traffic flow prediction result is obtained through the fully connected layer. Finally, a 60-minute traffic flow prediction experiment is carried out using two highway traffic datasets PEMSD4 and PEMSD8. The experimental results show that: compared with the spatio-temporal graph convolutional network (STGCN) model with good performance in the baseline model, the MAE (mean absolute error) value of the prediction results of the proposed STACNN model on the two datasets is improved by 2.79% and 1.18%, the MAPE (mean absolute percentage error) value increased by 1.00% and 0.46%, and the RMSE (root mean square error) value increased by 3.8% and 1.25%, respectively. In addition, introducing dilated causal convolutional neural network and spatial attention mechanism have positively contributed to extraction of spatial-temporal dynamic correlation features.
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