计算机工程2026，Vol.52Issue(5)：456-466,11.DOI:10.19678/j.issn.1000-3428.0070124
基于自适应图卷积优化元图学习的非平稳交通流预测研究
Research on Non-Stationary Traffic Flow Forecasting Based on Adaptive Graph Convolution with Optimized Meta-Graph Learning
摘要
Abstract
Owing to the non-stationary nature of traffic flow,extracting its dynamic spatial-temporal features is challenging.The non-smooth characteristics of traffic flow causes dynamic changes in different traffic modes and within different neighborhoods.To address this issue,this study proposes a Meta-graph learning traffic flow prediction model based on Adaptive Graph Convolution(Meta-AGC).Specifically,a method is designed to adaptively capture the spatial correlation between nodes in different traffic modes and the dynamic changes in traffic flow within different neighborhoods.The method pattern-matches the spatial-temporal features captured by AGC with a meta-node library in meta-graph learning,which enables the spatial-temporal meta-graph generated based on the meta-node library to adaptively represent the spatial correlations among nodes in different traffic modes.AGC consists of a set of graph wavelets with different learnable scales and a context attention mechanism to dynamically adjust the convolution receptivity field according to the input traffic flow information at any time.Consequently,the limitation of fixed acceptance domain in traditional convolution is overcome,and traffic flow variations within different neighborhoods triggered by random events are captured efficiently.Experimental results demonstrate that Meta-AGC enhances the prediction accuracy by 5.2％and 4.2％compared with the superior baseline model at the 6-step and 12-step prediction intervals,respectively.Additionally,the findings substantiate the assertion that Meta-AGC is more effective in modeling the non-stationarity of traffic flow and improving prediction accuracy.关键词
交通流预测/元图学习/图神经网络/注意力机制/自适应图卷积Key words
traffic flow forecasting/meta-graph learning/graph neural networks/attention mechanism/Adaptive Graph Convolution(AGC)分类
信息技术与安全科学引用本文复制引用
张红,朱思雨,张玺君,魏轿云..基于自适应图卷积优化元图学习的非平稳交通流预测研究[J].计算机工程,2026,52(5):456-466,11.基金项目
甘肃省重点人才项目(2024RCXM57) （2024RCXM57）
甘肃省重大专项计划(25ZYJA037) （25ZYJA037）
国家自然科学基金(62566036,62363022). （62566036,62363022）
