Optimization Method And Simulation Research On Automatic Vehicle Trajectory And Dynamic Signal Cooperative Control

With the continuous growth of car ownership and the increasing degree of motorization,the problem of urban traffic congestion has become more and more prominent.Experience has shown that the means of improving the road network capacity through refreshing infrastructure(eg,building/expanding roads)is no longer applicable to the current needs of the current transportation system.In recent years,with the emerging development of intelligent transportation technology,how to effectively utilize vehicle trajectory information for accurate traffic estimation,control and optimization,and thus improve the efficiency and reliability of the transportation system are hot and difficult issues in the field of intelligent transportation.The transition from a traditional manual driving system to a purely automated vehicle transportation system takes a long time.Therefore,this paper studies the optimization of automatic vehicle trajectory and traffic signal control at intersections for the traffic flow environment of manual driving and automatic vehicles.First of all,the secondary development of the VISSIM simulation platform is performed using Python and C++ language and a program framework that can provide simulation environment for vehicle trajectory and dynamic signal cooperative control strategy is developed.Secondly,this paper proposes a multi-mark-recognition vehicle trajectory optimization model by considering the road environment of automatic car and non-automobile car.Thirdly,we propose a two-layer optimization model which combines the vehicle trajectory optimization with the intersection signal optimization algorithm.Finally,in order to demonstrate the validity of the proposed approach,a experiment is carried out with different scenarios(degree of saturation,penetration rate of automated vehicles).The experimental results show that in under saturation conditions,when the automatic vehicle penetration rate reaches 30%,the average delay and travel time of the intersection are reduced by 20.95% and 13.91%.For oversaturated intersection,the optimization effect become obvious when penetration rate reaches 10%,at which the average delay and travel time are reduced by 13.78% and 10.77% respectively.