Research On Methods Of Short-term Traffic Flow Prediction Based On Graph Fourier Transform And Deep Learning

With the development of social economy,the existing highway infrastructure has been difficult to meet people's travel requirements.Traffic congestion on highways and urban roads has become a difficult problem for traffic management departments.With the rapid development of information,control and network technology,Intelligent Transportation Systems(ITS)are recognized as the main way to solve traffic congestion.In recent years,the emergence of new technologies such as Internet of things,Internet plus,big data and artificial intelligence has brought new vitality into ITS.It has become a hot topic in ITS research how to make use of the latest artificial intelligence technology to predict the traffic conditions,so that travelers can choose the right route,and reduce travel time,traffic congestion and environmental pollution.Based on the analysis of the research situation of traffic flow prediction at home and abroad,the spatiotemporal correlation characteristics of traffic flow are analyzed qualitatively and quantitatively with Pearson correlation coefficient and correlation function.Aiming at the time characteristic of traffic flow,the similarity and periodicity of traffic flow in working and non-working as well as the same continuous working days are analyzed,which provides a theoretical foundation for data set selection of traffic flow forecasting model.In terms of spatial characteristics,the correlation and local similarity of traffic flow in different sections are analyzed.The results show that the correlation of traffic flow in different sections is related to the distance between sections,but the correlation degree is not inversely proportional to the distance between sections,which provides a theoretical foundation for the construction of traffic flow prediction model.By analyzing the causes of abnormal traffic data,an abnormal traffic data detection method is proposed based on the local Leida criterion on the basis of analyzing the methods of Chauvenet,Dixon and Leida,and the experiments verify the reliability and effectiveness of the proposed method in abnormal traffic data detection.The reasons are analyzed for the lack of traffic data.According to determinate missing,a traffic data imputation method is proposed based on improved low rank matrix decomposition.The performance of the proposed method is tested and analyzed by the actual traffic flow data,which shows that the proposed method is effective.Based on the basic theory of discrete graph signal processing and the spatiotemporal correlation characteristics of traffic flow,a road network traffic flow adjacency matrix is constructed based on traffic data and distance correlation.A traffic flow decomposition algorithm is proposed for time series and road network based on Graph Fourier Transform,which decomposes traffic flow data into basic trend items and random fluctuations.Considering the temporal and spatial correlation of traffic flow,KNN algorithm is used to select related traffic flow data in different sections of the road network.Based on the decomposition of traffic flow data with Graph Fourier Transform,and the advantages of LSTM in processing time series,a hybrid traffic flow forecasting model called KNN-LSTM is proposed,which combines KNN,GFT and LSTM.The test results show that the proposed method has better prediction performance compared with classical ARIMA,SVR,ANN,DBN and LSTM models.In view of the current situation that there are few studies on traffic flow prediction in large-scale road network,a network traffic flow prediction method is proposed based on K-means clustering analysis and matrix decomposition.The traffic flow in different road sections is analyzed with K-means clustering in the road network,and the road sections are divided into K subsets using Euclidean distance as criterion.For each subset,a road section is randomly selected as the representative to construct the compression matrix of the road network data.The mapping relationship is calculated between the original road network data with the constructed compression matrix,and a traffic flow prediction model is constructed for selected sections.Finally,the predicted traffic flow in all sections is obtained through the mapping relationship in the whole road network.The experimental results show that the proposed method not only greatly improves the operation time,but also guarantees the prediction accuracy,and it is an effective method for large-scale road network traffic flow prediction.