吉林大学学报（信息科学版）2025，Vol.43Issue(2)：288-295,8.
基于双向多注意力图卷积的行程时间预测方法
Travel Time Prediction Method Based on Bidirectional Multi-Attention Graph Convolution
摘要
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.关键词
行程时间预测/图注意力/时空图卷积/马尔科夫链/深度学习Key words
travel time prediction/figure attention/spatio temporal graph convolution/markov chain/deep learning分类
计算机与自动化引用本文复制引用
邢雪,唐磊..基于双向多注意力图卷积的行程时间预测方法[J].吉林大学学报（信息科学版）,2025,43(2):288-295,8.基金项目
吉林省教育厅产业化培育基金资助项目(JJKH20230306CY) （JJKH20230306CY）
吉林省科技发展计划基金资助项目(20210101416JC) （20210101416JC）
