Research On Variable Speed Control In Lane Drop Of Expressway For Mixed Traffic

lanes drop of highway lanes is a common type of fixed traffic bottleneck,and it’s existence often leads to degradation of traffic system performance.Some research shows that speed limit on the upstream of the lane drop zone can effectively alleviate the negative effect of such bottlenecks on traffic.With the popularization of autonomous driving,a new type of traffic flow that mixes connected autonomous vehicles(CAVs)and ordinary vehicles has emerged.How to use the CAVs as a starting point to alleviate the negative effects of traffic bottlenecks has become an urgent problem to be solved.Therefore,this article focuses on the new traffic flow of hybrid connected vehicles and ordinary vehicles,and carries out the research on the speed limit control strategy of the highway lane drop area under the mixed traffic flow,aiming to use the connected vehicles(CAVs)as the traffic control system’s collector and speed limit controller to reduce the negative effects of lane drop area.The research content includes:(1)In view of the problem of calculating the upstream speed limit value in the lane drop zone of mixed traffic flow.First,consider factors such as the penetration rate of connected vehicles,the speed limit of the road section,and the sudden change of traffic capacity.The second-order traffic flow model METANET is improved to characterize the macroscopic traffic flow characteristics of the highway lane drop area.Then,based on the MPC idea,combined with the improved METANET model,the upstream speed limit controller is constructed with the optimization of traffic efficiency as the objective function.(2)Aiming to solve the problem of data sampling and speed control of individual CAVs in the speed limit control of mixed traffic,using SUMO simulation software,based on the CACC model and Wiedamann99 model to build a highway lane drop zone mixed traffic simulation environment.At the same time,in order to ensure the effectiveness of the MPC upstream speed-limiting controller in the micro-simulation environment,considering that conventional calibration algorithms such as genetic algorithm will fall into the local optimal solution and high complexity when calibrating,the AFSA is used to calibrate the controller.The macro METANET traffic model is calibrated with parameters.(3)Considering the difference of vehicle driving in mixed traffic flow,a real-time traffic state estimator based on CAVs is proposed.The RBF-BP neural network is train by the connected autonomous vehicle driving data output by the SUMO software.Then,the estimated value of the traffic state output by the neural network is used to eliminate the abnormal points by Kalman filter,and the design of the real-time traffic state estimator is completed.(4)In order to comprehensively explore the effect of only using CAVs for speed limit control,a set of speed limit optimization schemes for mixed traffic lane drop areas based on connected automatic vehicles is proposed,which combines real-time traffic state estimator and MPC upstream controller.Use Python language,based on SUMO’s Tra CI interface for programming to achieve simulation experiments.The final simulation results show that the speed limit scheme under mixed traffic flow constructed in this paper does not rely on the fixed vehicle detector and the VMS,but only uses CAVs in the mixed traffic flow has effectively reduced the lane drop area negative bottleneck effects and improve traffic capacity and efficiency.