Short-term Traffic Flow Forecast of Expressway Based on STAtt-DGCN Model
Accurate prediction of short-time traffic flow is an important means of fine supervision of highway traffic operation status,which helps to monitor potential traffic congestion events on highways in advance and control them in time.Scholars at home and abroad have proposed a variety of short-time traffic flow prediction methods from mathematical statistics and data-driven dimensions.Although the results are quite fruitful,the joint modeling capa-bility of traffic flow data is still insufficient in terms of temporal correlation and spatial correlation,resulting in that there is still improvement room in prediction accuracy.Based on this,a spatiotemporal attention diffusion graph convolution model(STAtt-DGCN)is proposed for the short-term prediction of highway traffic flow in this study.Relying on the classical temporal attention mechanism,spatial attention mechanism and graph convolution network,a space-time block,a spatiotemporal convolution block and a diffusion graph convolution network block are de-signed to establish the correlation of traffic flow data in the temporal and spatial dimensions,so that the prediction accuracy can be effectively improved.In this study,a 3-month ETC dataset of a highway in Jiangxi Province is se-lected to verify the performance of the proposed model;The common baseline models such as ARIM A,LSTM,and STGCN are chosen for the comparative evaluation of the models.The experimental results show that the STAtt-DGCN model exhibits better prediction ability of dataset almost every month.Taking the data in April 2022 as an example,compared with the most challenging STGCN baseline model,the proposed model shows a decrease of 17.9％,40.0％,and 11.0％in mean absolute error,mean square absolute error and mean absolute error respectively.This shows that the prediction accuracy of the STAtt-DGCN model is greatly improved compared with the baseline method,and can be applied to the accurate prediction of highway traffic flow.
万方数据
维普
