Research on Urban Road Traffic Flow Prediction Based on Sa-Dynamic Graph Convolutional Neural Network

Song Hu, Jian Gu () and Shun Li
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Song Hu: Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, China
Jian Gu: Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, China
Shun Li: School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410004, China

Mathematics, 2025, vol. 13, issue 3, 1-18

Abstract: Neural network models based on GNNs often achieve good results in traffic flow prediction tasks of traffic networks. However, most existing GNN-based methods apply a fixed graph structure to capture spatial dependencies between nodes, and fixed graph structures may not be able to reflect the spatiotemporal changes in node dependencies. To address this, introducing a self-attention mechanism applied to an adaptive adjacency matrix, the neural network architecture is improved based on Graph WaveNet, and a new approach called self-attention dynamic graph wave network (SA-DGWN) is proposed, which can fit the spatiotemporal dependencies of the road network. In an experiment, traffic flow data were extracted based on RFID from certain roads in Nanjing, China. The results show that under the same configuration, compared to Graph WaveNet, MAE, MAPE, and RMSE from the proposed method reduced by 3.08%, 3.68%, and 2.6%, respectively. In addition, for the training data, we explored the impact of temporal feature and sampling periods on the training effect. The additional results indicate that adding hour-minute-second information to the input improved the model’s accuracy, reducing MAE, MAPE, and RMSE by 15.28%, 12.28%, and 14.01%, respectively. Adding day-of-the-week features also brought substantial performance improvements. For different sampling periods, the model performed better overall with a 10 min sampling period compared to 5 min and 15 min periods. For single-step prediction tasks, the longer the sampling period, the better the prediction effect.

Keywords: traffic flow forecasting; deep learning; graph convolutional network; self-attention mechanism; Graph WaveNet (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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