Abstract: To address the challenge of efficiently mining spatiotemporal information for traffic prediction, a novel vehicle travel time prediction method is proposed based on bidirectional multi-attention spatiotemporal graph convolution. To extract the spatial dependencies within the road network, a traffic transfer matrix is constructed using a Markov chain approach, which captures the bidirectional traffic flow transfer relationships. Graph convolution is employed to learn the spatial dependencies within the graph network. Subsequently, an attention mechanism is utilized to capture both local and global temporal features within the traffic flow map. Finally, a MLP ( Multi-Layer Perceptron) is used to forecast travel times, producing the final prediction results. The Xuancheng road network traffic data is selected for model validation. The results demonstrate that the proposed model reduces the RMSE (Root Mean Square Error) by 7. 6% , 3. 7% , and 9% , respectively, compared to baseline models such as STGCN( Spatio-Temporal Graph Convolutional Networks), ASTGCN(Attention Based Spatial-Temporal Graph Convolutional Networks), and A3T-GCN ( Attention Temporal Graph Convolutional Network). This significant reduction in RMSE indicates that this model substantially improves prediction accuracy, highlighting its effectiveness in capturing and utilizing spatiotemporal information for more precise traffic predictions.

Key words: travel time prediction, figure attention, spatio temporal graph convolution, markov chain, deep , learning