Study On Methods Of Traffic Estimation Under Connected And Autonomous Vehicles And Manual Vehicles Mixed Traffic Flow

With the rapid growing of vehicle ownership in China recently,traffic demand of road traffic is increasing along with it.At the same time,the slowing grows of highway construction,as well as the highway toll free policy in red-letter day makes the contradiction of traffic supply and traffic demand sharper.At the present stage of development has not allow us to continue to expand the road construction,and then improving the level of highway operation and management is the only way to alleviate the problem.The basis of all operations management is the overall perception of highway traffic state,the road density is particularly the important one.In addition,the speedy development of machine learning technology,video processing technology,and sensor technology brought about changes in vehicles.Many Internet Company and traditional car companies turn their eyes on the intelligent vehicle research and development,and many practical techniques have been applied in the vehicle,such as adaptive cruise system,autonomous parking system and so on.The actual intelligent vehicle in the road will not be long.In this case,the future road traffic environment will be smart cars and manual cars mixed situation for a long time.This paper is established in the connected and autonomous vehicles and manual vehicles mixed environment,and research the method of highway density estimation.First,in order to realize the connected and autonomous vehicles and manual vehicles mixed environment,this thesis presents a comprehensive understanding of the existing traffic models.Combined with the analysis of the characteristics of connected and autonomous vehicle driving behavior,thesis has choose a suitable model to simulate the connected and autonomous vehicles.The parameters of this model were calibrated by the NG-SIM database,and then control strategy of the connected and autonomous vehicle are proposed,model parameters of each strategy are also described.Secondly,the fundamental diagrams of traffic flow is studied.In order to determine the macroscopic traffic parameters of the mixed traffic flow,second development function of the Vissim is selected.And then creating the simulation of the connected and autonomous vehicles and manual vehicles mixed environment,which the vehicle simulation model and parameters are mentioned above.On the basis of these experiments,the macroscopic traffic parameters of mixed traffic flow are obtained.Then,while the penetrance of the connected and autonomous vehicles reaches 40%,that the connected and autonomous vehicles’ average speed can accurately characterize the link average speed is found through the numerical simulation.And the mixed traffic flow is divided into two categories.Since then,two road density estimation methods,kalman filtering method and fusion estimation method based on BP neural network,are proposed.Their application scenarios are also discusses in the latter.Finally,the simulation scenario is designed to validate the proposed method.The thesis concludes that the superiority of those two methods is related to traffic flow and the penetrance of the connected and autonomous vehicles.While the traffic is free flow,Kalman filter estimation method is chosen.And while the traffic is confused or the traffic flow has phase transition,BP neural network fusion method is better.