Lane Opening And Closing Configuration Of Expressway Toll Station Based On Traffic Flow Prediction

In the post-ETC(Electronic Toll Collection)era,the charging methods of China’s expressway toll stations are gradually diversified,and the existing lane opening and closing configuration methods have been difficult to adapt to the traffic demand under this charging mode,resulting in uneven distribution of toll station resources and serious polarization of traffic congestion and lane idleness.Therefore,this paper relies on the toll data of expressway toll stations,introduces deep learning technology on the basis of traditional queuing theory,and carries out research on the opening and closing configuration of toll lanes from the perspective of traffic demand and supply.The main research contents include the following four aspects:(1)This paper analyzes the basic characteristics of toll stations,and carries out data acquisition and processing.Firstly,this paper analyzes the basic characteristics of toll stations in China,selects the airport toll station of Hebei Xinyuan Expressway to carry out field research,and obtains the data of traffic volume,the proportion of vehicle types and service time.Then,the data processing and analysis are carried out.The results show that the traffic volume of the toll station has time-varying characteristics,and the proportion of ETC users reached about 70%.(2)The M/G/K queuing model based on multiple charging methods is proposed.Firstly,a statistical analysis of the service time obtained from the survey is carried out according to different charging methods.Then,according to the coexistence of ETC and MTC(Manual Toll Collection)lanes and the characteristics of diversified charging,the M/G/K model is selected to study the toll station.Then,the calculation methods of the mean and variance of service time in the M/G/K model are improved for the ETC and MTC queuing systems respectively,and a method to estimate the capacity of toll lanes by using the average service rate is proposed.(3)A traffic flow prediction model of toll station based on BOA-LSTM is proposed.Firstly,according to the time-varying characteristics of the traffic flow of the toll station,the traffic volume and the proportion of vehicle types are selected as the prediction indicators,and the LSTM prediction model is established,and Adam is selected as the gradient descent algorithm for model training through experimental comparison.Then,in view of the difficulty in determining the number of hidden layer nodes and the learning rate in the LSTM model,the Bayesian optimization algorithm(BOA)is used to optimize the hyperparameters of the LSTM model.Then,the prediction effect of the model is verified by taking the airport toll station as an example.The results show that the model optimized by the Bayesian algorithm has a better prediction effect.Compared with before optimization,the root mean square error(RMSE)of traffic volume is reduced by 11.43,and the mean absolute percentage error(MAPE)of traffic volume and the proportion of vehicle types are reduced by 4.47% and 0.5%,respectively.(4)A lane opening and closing configuration model based on the comprehensive cost optimization is constructed.Firstly,aiming at the problem of uneven allocation of toll lane resources,considering the operating cost of the toll station and the traveler’s time delay cost,an optimization model is constructed which takes the minimum comprehensive cost as the goal,takes the traffic flow forecast as the input and the number of lanes as the output.Then,the particle swarm algorithm(PSO)is used to solve the model.Then the control variable method is used to analyze the influence of traffic volume,the proportion of vehicle types and other factors on the model.Finally,an empirical analysis is carried out,and the results show that,compared with the current scheme,the method proposed in this paper can reduce the comprehensive cost on the premise of ensuring a good service level.The average daily comprehensive cost of working days can be reduced by 175.92 yuan,a reduction of 2.30%,the average daily comprehensive cost of rest days can be reduced by 605.52 yuan,a decrease of 5.14%.