A Method for Real-time Detecting Freeway Moving Bottlenecks Using Intelligent Connected Vehicles
Aiming at the problem that the fixed-point detection method cannot effectively monitor the formation and evolution of the mobile bottleneck,a real-time detection method of the mobile bottleneck on the expressway based on intelligent networked vehicles is studied.A wavelet analysis-based method is proposed to reduce the errors of tra-jectories collected by intelligent connected vehicles(ICVs).And then the key points that represent the change of traf-fic states are identified based on the relationship between the vehicle trajectories and the traffic states.Considering that multiple traffic congestions may simultaneously occur on a road segment,an algorithm is proposed to classify the key points based on the space-time characteristics of traffic shockwaves.Finally,the traffic shockwave speed is calculated,and moving bottlenecks are identified and evaluated.Based on SUMO simulation platform,experiments are carried out on the detection effect of mobile bottleneck location,propagation speed and queuing delay under the proportion of various intelligent vehicles in Hujia freeway.The results show that when the penetration rate of ICVs is less than 10％,the accuracy of traffic wave speed estimation improves by an average of 20％after trajectory de-noising.When the penetration rate exceeds 3％,the estimation error of the moving bottleneck propagation speed is below 0.42 m/s.When the penetration rate reaches 7％,the estimated position of the moving bottleneck has a devia-tion mostly within 10 m,with a maximum of 25 m.The proposed method can detect the presence of freeway bottle-necks which occur randomly and evaluate their severity in real-time.
万方数据
维普
