Traffic Congestion Recognition Based On CTM In The Mixed Flow Of Driverless And Manual Driving

With the rapid development of economy,people's demand for the convenience,comfort and safety of travel is increasing day by day.The research on driverless vehicle emerges as the times require.The research on driverless technology is the trend of the times,and driverless vehicle is an important part of future road traffic flow.Therefore,the research on driverless vehicle's following behavior,the characteristics of mixed traffic flow composed of driverless and manual driving vehicles,the mechanism of future mixed traffic flow congestion,and the methods to alleviate future mixed traffic flow congestion are imminent.This paper takes the future mixed traffic flow as the research object,studies the influence of the proportion of driverless vehicles on traffic congestion,and then identifies the traffic congestion under the mixed flow.Firstly,the following characteristics of driverless vehicle are analyzed,and IDM(Itelligent Diver Model)model is the research foundation of the basic car following model of driverless vehicle.Through the analysis of the classic IDM model,this paper puts forward two problems: the non negative expected distance and the slow acceleration,and improves the IDM model.The improved IDM model uses the measured data in ngssim for parameter calibration and model verification,and finally determines the car following model of driverless vehicle.Secondly,on the basis of the existing research on the following model of the manual driving vehicle,the representative models of different traffic are selected as the research basis and analyzed.Through parameter calibration and model verification,the IDDM(improve desire distance model)model with high simulation accuracy and good stability of various error indexes is determined as the following model of the manual driving vehicle.Then,the basic traffic flow diagram of different types of vehicles in different proportions is deduced by the determined following model of driverless vehicles and following model of driverless vehicles.Through the simulation of the basic traffic flow diagram,the relationship between the flow and density of mixed traffic flow is obtained.Therefore,the influence of driverless vehicles on the macro parameters of mixed traffic in different proportions is determined and fit the relationship between the proportion and the maximum traffic flow and free flow of mixed traffic flow.Finally,on the basis of the classical cellular transmission model,using the relationship between the proportion of driverless vehicles and the maximum traffic flow and free flow of the mixed traffic flow,the cellular transmission model under the mixed traffic flow environment is established,and the average travel speed is selected as the evaluation index of traffic congestion.Through simulation,the speed indexes of each road section under different proportion are obtained based on the analysis of the impact of different proportion of road network congestion,it is concluded that the larger the driverless vehicle is,the more conducive to alleviate traffic congestion,which provides a theoretical basis for future mixed traffic congestion identification.