计算机工程2025，Vol.51Issue(9)：139-148,10.DOI:10.19678/j.issn.1000-3428.0069439
基于多时空图融合与动态注意力的交通流预测
Traffic Flow Prediction Based on Multiple Spatio-Temporal Graph Fusion and Dynamic Attention
摘要
Abstract
Accurate traffic flow prediction is a key prerequisite for realizing intelligent transportation systems,and is of great significance for strengthening system simulation and control and improving the decision-making of managers.To address the problem of most existing Graph Convolutional Network(GCN)models ignoring the dynamic spatial and temporal variations in traffic data and insufficiently employing node information,which leads to insufficient extraction of spatial and temporal correlations,a traffic flow prediction model based on multiple spatio-temporal graph fusion and dynamic attention is proposed.First,the temporal characteristics of traffic flow data in multi-temporal states are extracted by different convolutional cells.The next step involves constructing a multiple spatio-temporal graph to capture the dynamic trend and heterogeneity of nodes in spatial distribution,followed by extracting spatial characteristics through the integration of GCN.Finally,the spatial and temporal characteristics are analyzed and fused using the multi-head self-attention mechanism to output prediction results.Experimental analyses are performed on two public datasets,PeMS04 and PeMS08,and compared with the Attention Based Spatial-Temporal Graph Convolutional Network(ASTGCN),Multiview Spatial-Temporal Transformer Network(MVSTT),Dynamic Spatial-Temporal Aware Graph Neural Network(DSTAGNN)and other benchmark models that utilize spatio-temporal graph convolution.The results show that the Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and Root Mean Square Error(RMSE)of the proposed model are reduced by 7.10％,7.22％,and 6.47％,respectively,demonstrating the proposed model's strong adaptability and robustness.关键词
智能交通系统/交通流预测/时空特征/图卷积网络/多头自注意力机制Key words
intelligent transportation system/traffic flow prediction/spatio-temporal characteristics/Graph Convolution Network(GCN)/multi-head self-attention mechanism分类
信息技术与安全科学引用本文复制引用
翟志鹏,曹阳,沈琴琴,施佺..基于多时空图融合与动态注意力的交通流预测[J].计算机工程,2025,51(9):139-148,10.基金项目
国家自然科学基金面上项目(61771265) （61771265）
江苏高校"青蓝工程"项目 （）
南通市科技计划项目(JC2021198). （JC2021198）
