计算机工程2026，Vol.52Issue(8)：16-32,17.DOI:10.19678/j.issn.1000-3428.0260397
时滞感知注意力驱动的动态图卷积交通流预测模型
Dynamic Graph Convolutional Traffic Flow Prediction Model Driven by Time-Lag Aware Attention
摘要
Abstract
Traffic flow prediction is a key core technology in Intelligent Transportation Systems(ITS)and has significant value in improving the efficiency of urban traffic management.Time series prediction and machine learning models are widely used in traditional methods;however,spatio-temporal Graph Neural Networks(GNN)have become a research hotspot owing to their outstanding advantages in traffic flow representation learning.However,existing methods have significant limitations.First,they rely on static graph structures,making it difficult to model long-range spatial dependencies and regional differences.Second,they fail to capture the time delay effect between road segments,resulting in insufficient modeling of spatio-temporal dependencies.To address these issues,this study proposes a Space Dynamic Graph Convolutional(SDGC)traffic flow prediction model based on the Time-Lag Aware Attention(TLAA)mechanism.The encoder-decoder architecture is adopted,and the mechanism is combined with spatial dynamic graph convolution to mitigate the adverse effects of the time-delay phenomenon on prediction accuracy.Specifically,gated causal convolution incorporates attention mechanisms to explicitly model the asynchronous temporal dependencies of spatial nodes by learning delay parameters.The dynamic adaptive spatial correlation matrix can perceive real-time changes in traffic status and accurately depict the dynamic propagation process of traffic flow in the road network.Meanwhile,the introduction of spatio-temporal embedding enables the model to accurately depict the short-term fluctuations,long-term periodic patterns,and spatio-temporal heterogeneity of traffic flow under network topology constraints.Experiments on public benchmark datasets demonstrate that this method effectively addresses challenges related to time lag and dynamic behavior.Compared with existing state-of-the-art baseline models,it achieves significant reductions in key prediction error metrics Mean Absolute Error(MAE)and Root Mean Square Error(RMSE),ranging from 6.5％to 11.9％.关键词
交通流量预测/时滞感知/注意力/门控因果卷积/动态自适应空间Key words
traffic flow prediction/time-lag aware/attention/gated causal convolution/dynamic adaptive space分类
信息技术与安全科学引用本文复制引用
魏斯玮,袁景凌..时滞感知注意力驱动的动态图卷积交通流预测模型[J].计算机工程,2026,52(8):16-32,17.基金项目
国家自然科学基金(62472332). （62472332）
