广东工业大学学报2026，Vol.43Issue(3)：54-63,10.DOI:10.12052/gdutxb.250045
基于动态图多时间视野注意力的交通流量预测
Traffic Flow Prediction Based on Dynamic Graph Multi Temporal Perspectives Attention Network
摘要
Abstract
Traffic flow prediction is an important technology in intelligent transportation systems(ITS).Accurate traffic prediction can reduce congestion and improve traffic efficiency.However,traffic flow data contains complex temporal relationships,and capturing dynamic traffic spatial relationships is a challenge.In order to improve the prediction accuracy,a dynamic graph multi temporal perspectives attention network(DGMAN)is proposed,based on the spatiotemporal data of traffic flow.The model uses a dynamic graph learning module(DGLM)to extract the dynamic relationship information between traffic nodes in traffic data by establishing a dynamic graph.In complex temporal data,the multi temporal perspectives attention mechanism(MtpA)captures the temporal dependence of traffic flow and mines potential temporal relationships.Finally,the proposed model is tested on 4 real-world datasets.Compared with the baseline models,DGMAN achieves the best performance in the mean absolute error(MAE),root mean square error(RMSE)and mean absolute percentage error(MAPE)evaluation metrics.关键词
交通流量预测/动态图/多头注意力Key words
traffic flow prediction/dynamic graph/multi-head attention分类
信息技术与安全科学引用本文复制引用
柴文光,刘俊贤..基于动态图多时间视野注意力的交通流量预测[J].广东工业大学学报,2026,43(3):54-63,10.基金项目
国家自然科学基金资助项目(61902232) （61902232）
广东省自然科学基金资助项目(2022A1515011590) （2022A1515011590）
