Abstract:

A traffic flow prediction model based on a multi-dimensional attention mechanism was proposed to achieve precise traffic flow prediction and enhance the intelligent management level of expressways. Comparative experiments were conducted on real traffic datasets from the Zhangji Expressway to verify the accuracy and predictive accuracy of the model. The model extracted spatial and temporal features of traffic flows using graph neural networks (GNN) and temporal convolutional networks (TCN), respectively. It integrated a multi-dimensional attention mechanism to mine key information within spatiotemporal data. Additionally, a multi-task learning architecture was introduced, employing a loss function based on homoscedastic uncertainty to balance the joint learning of different tasks, thereby enhancing the generalization ability and robustness of the model. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of the model on the test set are 7.467 and 5.133, respectively, demonstrating superior predictive accuracy compared to baseline models. The proposed prediction method can effectively uncover the spatiotemporal characteristics of traffic flows, describe the actual state of traffic operations, and make accurate predictions of the traffic flow on expressways.

Key words: traffic flow prediction, graph neural network (GNN), temporal convolutional network (TCN), multi-dimensional attention mechanism