时空图神经网络在交通流预测研究中的构建与应用综述OA北大核心CSTPCD

The prediction of traffic flow is a pivotal concern within urban traffic management and planning,yet conven-tional forecasting techniques prove inadequate in addressing challenges like data sparsity,nonlinear associations,and intri-cate dynamics.Graph neural network is a deep learning approach based on non-Euclidean structural data,which has been widely used in various complex network modeling and predictive tasks in recent times.To address traffic flow prediction,a spatiotemporal graph neural network is proposed,which can capture spatial and temporal correlations,making signifi-cant progress compared to earlier predictive models.An analysis is conducted on models utilizing spatiotemporal graph neural network for the prediction of traffic flow in recent times Firstly,various construction methods of adjacency matri-ces are summarized and compared.Then,the common components of traffic flow prediction models are listed from the perspective of spatial correlation and temporal correlation,and different spatio-temporal fusion modes are classified and compared.On the application front,spatiotemporal graph neural network models are categorized into three classes based on temporal scales:long-term prediction,short-term prediction,and combined long-short-term prediction.Analysis of respective objectives and requisites is conducted,accompanied by enumeration and comparison of prominent recent models.Finally,limitations of existing research are deliberated upon,and prospects for future studies pertaining to rele-vant models are outlined.