Traffic Demand Analysis And Traffic Effect Modeling And Simulation Of Expressway Toll Station

With the steady development of our country’s economy and the continuous optimization of economic structure,the passenger and freight volume of expressway is increasing.Therefore,the traffie pressure of expressway toll stations is increasing day by day.Coupled with the impact of toll delay on traffic capacity,the congestion of expressway toll stations is becoming more and more serious.And consequently toll stations have gradually become the bottleneck of Expressway traffic.Because of land,capital,cost and environment,the cost of reconstruction and expansion of is a reliable way to solve the problem.Reliable prediction of future traffiexpressway is very high.As a result,starting from the perspective of accurate management c flow at toll stations is an important basis for traffic decision-making.Only by making effective decisions based on accurate and reliable prediction can the congestion problems faced by toll stations on expressways be effectively solved,and the level of traffic service can be constantly improved.Expressway toll station traffic flow has the characteristics of sudden and non-linearity.Elman neural network model not only realizes the static system modeling,but also reflects the dynamic process directly through state feedback,memory and output of historical data.It has the ability to adapt to time-varying characteristics.Compared with BP neural network and other general neural networks,Elman neural network model has remarkable advantages and very good adaptability because of the ability of calculation and network stability.Therefore,Elman neural network can be used to forecast the traffic volume of expressway.Based on the detailed analysis of the existing expressway traffic flow forecasting models,this paper studies the Elman neural network forecasting model based on time series.Combining with the characteristics of Dengfeng East Toll Station traffic flow,the Elman neural network model of Dengfeng East Toll Station monthly average daily traffic flow is established by using MATLAB software and verified.In the process of pretreatment of original data,the normalization method of data is improved to make it more suitable for medium and long-term traffic flow prediction.The results show that the improved Elman neural network is very suitable for traffic flow prediction of toll stations,and the prediction results are fast and accurate.According to the behavior characteristics of expressway toll station,the traffic capacity of existing exit lanes of Dengfeng East toll station is analyzed.The prediction results of Elman neural network model are used to model,simulate and evaluate the existing Lane configuration of Dengfeng East toll station by VISSIM software.The results show that the existing exit lane configuration of Dengfeng East toll station can fully meet the traffic demand in three years.We should focus on optimizing the capacity of single lane traffic and timely guidance of abnormal vehicles,which can effectively improve the traffic efficiency and service level.Because the influencing factors of expressway traffic flow are complex,it is necessary to use the predicted traffic flow data to forecast the traffic flow in the future.Therefore,it is difficult to grasp the accuracy of the forecasting results by using the traffic flow forecasting model constructed in this paper.Therefore,this study only predicts the monthly average daily traffic flow in the next three years.In the future,we need to further study the influencing factors of expressway traffic flow and improve the prediction methods.If we first establish a neural network model to predict the individual traffic flow factors accurately,and then use the predicted value of each traffic flow factor to establish a neural network model to predict the long-term traffic flow,we should greatly improve the accuracy of the long-term traffic flow prediction.