Research On The Method Of Expressway Traffic Volume Prediction Based On The Networking Toll Data

With the development of intelligent high-speed construction,travelers need to master the traffic state of Expressway in advance.The accurate prediction of traffic volume is an important part of intelligent transportation system,which can provide a theoretical basis for the formulation of traffic guidance strategy.The field information stored in the expressway network toll data mainly includes the traffic information such as the time and cost of vehicles entering and leaving the toll station,as well as the information of vehicles such as vehicle type and load.It has the characteristics of timely content update,large amount of data and easy access.In this study,the prediction of shortterm OD traffic volume is based on the analysis and training of large-scale historical data.From the dynamic traffic assignment method of loading OD data into the road network,case analysis and other aspects,the prediction method of expressway network traffic volume is studied.The basic idea is as follows: extract effective information by preprocessing the massive expressway network toll data,build the expressway network OD traffic volume prediction model,and then in order to load the predicted OD traffic volume into the road network,build a dynamic traffic assignment model,and then get the road section traffic volume in the prediction period.Based on the massive historical toll data of expressway network,this paper mainly completes the following research work(1)This paper introduces the format and field meaning of extracted expressway toll data,explains the research significance of toll data,obtains effective information from massive historical data through preprocessing,and analyzes the causes of abnormal data in toll data,so as to obtain more valuable traffic data information for research purposes.(2)Based on the traffic data obtained by preprocessing,analyzing the characteristics of expressway traffic flow.The periodic similarity of traffic volume and travel demand under different time scales reflecting the characteristics of time distribution,and the driving distance distribution reflecting the characteristics of traffic spatial distribution are analyzed.(3)Through the correlation analysis of the influencing factors of expressway OD traffic volume prediction,the prediction variables are determined.In this paper,onedimensional convolution filter is applied to the input feature extraction of long-short term memory(LSTM)neural network models.The mixed LSTM models are constructed,which only consider several time periods before the prediction point of the day,and consider several time periods before the prediction point of the day and historical data of the same period.The input and output of the model are determined,and then the prediction effect of the model is evaluated Indicators are selected.(4)In order to load the predicted OD data into the road network,a dynamic traffic assignment model based on traveler’s bounded rationality considering different vehicle types is constructed.By establishing an equivalent variational inequality model,the dynamic traffic assignment problem is transformed into a variational inequality problem.Finally,the model is solved by MSA algorithm.(5)According to the research content of this paper,the expressway network of J province is selected as the research object,and the case analysis is carried out based on the historical toll data extracted from the provincial toll system.Constructs the expressway network model by combining the basic geographic information files of J province and the longitude and latitude information of toll stations.Based on the hybrid LSTM network model constructed in this paper,we need to obtain the required OD data before forecasting OD traffic volume.It is mainly divided into two types: OD matrix of the same period in history and OD matrix of several periods before the forecast point of the day.The dynamic OD matrix of the same period of history can be obtained by summarizing the historical charging data according to the time interval,while it is difficult to obtain the OD matrix of the period before the forecast point of the day,and it is not the focus of this paper.Therefore,in the case study of this paper,the OD data of the day obtained from the collection of charging data is used to replace it,and the dynamic OD matrix of different vehicle models on the day of prediction is obtained;the prediction model is trained based on the massive historical OD traffic volume data,and its prediction performance is evaluated on the test set to compare the prediction effect of different prediction models;and then the prediction results of different prediction models are compared through the dynamic intersection The traffic assignment model loads the data into the road network,compares and analyzes the traffic at the link level,and verifies the effectiveness of the prediction method..