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OA

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.