Modleling And Simulation Analysis Of Highway Mixed Traffic Flow In Connected And Autonomous Environment

Highways are the arteries of transportation.Nowadays,autonomous driving technology and Internet of Vehicles technologies are becoming more and more mature.In the future,there will be more and more connected and autonomous vehicles on highways.Some connected and autonomous vehicles have begun to test and drive on the road.It is predicted that in the next few decades,the market share of intelligent networked vehicles will continue to increase.However,my country’s expressways are in a state of mixing cars and trucks.The addition of intelligent networked vehicles will inevitably have a greater impact on existing expressways.Therefore,it is necessary to study the conditions of each expressway in the intelligent networked environment.Traffic flow characteristics of vehicle models under mixed driving conditions.The thesis takes the characteristics of mixed traffic flow in the intelligent networked environment as the research purpose,applies the cellular automata to the two-lane highway traffic environment,and proposes two mixed traffic flow models: one is formed by manual driving cars and connected and autonomous cars.The mixed traffic flow model,in particular,takes two types of cars and trucks into account;the second is a mixed traffic flow model formed by a fleet of artificially driven cars and connected and autonomous trucks.After that,Mat Lab software was used to simulate the two models respectively,and the two intelligent networked mixed traffic flow models were studied through numerical analysis.Among them,after fully considering the characteristics of passenger cars and trucks and the characteristics of these two models after intelligent networking,the classic Na Sch model is improved to adapt to different types of vehicles and different car-following combinations,and a safe distance-based approach is proposed.The variable lane-changing model for different vehicle types establishes a mixed traffic flow model based on multiple vehicle types in an intelligent networked environment.At the same time,the random slowdown step of the classic Na Sch model is improved to adapt the artificially driven car to the joining of the connected and autonomous truck fleet,optimize the lane-changing model of the artificially driven vehicle,and refine the unit length of the cell.The car-following rules and teaming rules of connected and autonomous trucks establish a mixed traffic flow model of artificially driven cars and connected and autonomous truck fleets.A conclusion is reached through computer simulation analysis: artificially driven cars and connected and autonomous vehicles are mixed driving.With the increase in the market share of connected and autonomous vehicles,the traffic flow of roads is also increasing,and the road can be improved when the market share reaches 100%.The flow is about 1.64 times,and this effect has nothing to do with the ratio of small passenger cars to trucks on the road.At the same time,it can also reduce the frequency of vehicle lane changes and reduce the speed difference between vehicles in the system.In this mixed traffic flow,the larger the proportion of trucks,the smaller the average speed of the system,and the earlier the congestion density comes.The increase in the share of intelligent networked vehicles will help increase the average speed of the system;manual driving vehicles and intelligent networks As the density increases,the greater the proportion r of connected and autonomous trucks,the smaller the capacity of the traffic flow.Compared with connected and autonomous trucks traveling alone,the combination of the fleet can effectively improve the road traffic in the system.Traffic capacity,when the density is between 60 veh/km and 100veh/km,it can effectively reduce the frequency of lane changing of manually driven cars in the system.