Research On Traffic Flow Prediction Method Based On Temporal And Spatial Graph Neural Network

Recently,the number of car keepers are increasing,which causes a serious problem of imbalance on traffic supply and demand.The traffic flow data can directly reflects real-time traffic conditions.If reliable traffic information can be known in advance,it will help traffic managers to formulate and implement traffic planning strategies,effectively reducing public safety risks.At the same time,it can also help travelers to better plan their travel routes,reducing time cost and economic loss.Therefore,it is of great significance to the road managers and users.However,the spatio-temporal correlation between roads is complex and variable,and the traffic flow data is highly nonlinear and random,which makes the traffic flow prediction very challenging.At present,most of the existing methods do not effectively analyze the spatiotemporal correlation based on real-time traffic information and do not take into account the differences in data cycles at different time periods caused by human activities.To address these issues,this thesis proposes an adaptive convolution and multivariate data association traffic flow prediction method based on spatio-temporal graph neural network.The main contributions are as follows:(1)This thesis proposes Adaptive Convolution Graph Neural Network model(ACGNN).The model constructed by adaptive convolution block and gating recycling units.First,from the Angle of breadth,explore neighboring roads by adaptive breadth of convolution block function centering on the road,and then forget selectively through the depth function,in order to learn self convolution path and capture spatial dependencies according to the study of the path.Finally,the spatio-temporal correlation analysis was performed by combining the gated cyclic unit.(2)This thesis proposes Multisensor Data Correlation Graph Convolution Network(MDCGCN)model.The MDCGCN model consists of three parts: recent,daily period and weekly period components,and each of which consists of two parts: 1)benchmark adaptive mechanism block;2)multisensor data correlation convolution block.The first part can eliminate the differences among the periodic data and effectively improve the quality of data input.By analyzing the real-time changing relationship of traffic patterns among roads,the second part constructs a traffic pattern relationship graph and combines the original traffic topology graph to effectively analysis the spatio-temporal correlation.(3)To verify the reliability of the proposed method,a large number of experiments were carried out on traffic flow data sets,and compared with other methods.The experimental results show that the proposed method is superior to the comparison methods in all kinds of evaluation indexes.