Research On Expressway On-ramp Control And Simulation Based On Connected And Autonomous Vehicles

As an important component of road traffic,the merging area of highways is a conflict point connecting the main lane and ramps.The influx of ramp vehicles can lead to a decrease in the capacity of the merging area,which in turn affects the normal operation of the upstream and downstream of the road.The thesis is based on intelligent connected vehicles(CAVs)and establishes a multi-objective overall optimization model for highway confluence areas,exploring an efficient,stable,and economical intelligent connected vehicle collaborative control strategy.The specific research work is as follows:Firstly,analyze the traffic flow characteristics of the merging area and study vehicle behavior.Starting from typical car following models and lane changing models,this thesis elaborates in detail on the linear and nonlinear car following models,as well as the forced lane changing and free lane changing models in the car following model.Analyze the congestion mechanism of highway merging areas based on different traffic flow situations,establish a disturbance model based on the speed and headway of merging vehicles,conduct disturbance analysis on local areas,and provide headway conditions to avoid congestion.Then,based on the intelligent connected vehicle,a micro model is established to analyze the IDM intelligent driver model and explore the following law of the connected vehicle.By modeling and analyzing the minimum lane changing distance for vehicles,as well as the conditions for avoiding collisions,a lane changing model for connected vehicles is established.Conduct research on the merging time of vehicles under different initial speed conditions,and predict the merging time of connected vehicles.Finally,based on the intelligent connected vehicle,a confluence collaborative control strategy is proposed to build an information interaction environment for the CAVs confluence scenario.Establish objective functions and optimization models from three aspects: driving stability,driving efficiency,and fuel economy,and use Hamilton functions to analyze the objective functions.Using Python to simulate the traffic flow in the merging area of collaborative control CAVs,using Vissim to simulate the merging of uncontrolled HDVs(manually driven vehicles)under the same scenario and conditions,and establishing an evaluation model based on the delay time and fuel consumption during the merging process for evaluation.Experiments have shown that under the merging strategy,vehicles can adjust their speed and acceleration based on the distance between the vehicle heads to ensure the stability of the overall merging fleet.Under the same conditions,the delay time of CAVs is 47.804% less than that of HDVs;The fuel consumption of CAVs is reduced by 18.257% compared to that of HDVs.The research results of this thesis indicate that optimizing the control process of vehicles in highway merging areas based on intelligent connected vehicle technology can effectively improve merging efficiency and traffic flow stability,reduce fuel consumption and driving delay.