Study On Urban Traffic Signal Control Strategy In The Mixed Traffic Environment

With the development of modern society’s science and technology,economy and the continuous improvement of vehicle automation,autonomous vehicles are expected to be gradually promoted and applied to people’s production and life from the theoretical level.In the future,the vehicle composition of urban traffic will inevitably be a mixed situation of human-driven vehicles and autonomous vehicles.In the past,urban traffic problems are expected to be alleviated by various advantages of autonomous vehicles.However,at present,there are few studies on autonomous vehicles,and there are no clear theoretical studies on whether autonomous vehicles can really improve road traffic capacity and the degree of impact on road traffic flow under different proportions.In addition,it is still unknown how urban traffic signal control strategies should respond to the future human-machine mixed driving environment.Therefore,in order to study the urban signal control strategy in man-machine hybrid driving environment,the main research carried out in this paper is as follows:First of all,the main research content and technical route of this paper are clarified,and the basic concept and existing calculation model of signal-controlled intersection capacity are discussed in detail.Secondly,a calculation model of capacity correction for signalized intersections in mixed environment is proposed.The "flow-density" relationship of homogeneous traffic flow on single-lane road is derived by basic traffic flow diagram model,traffic flow equilibrium analysis and vehicle following model.According to the proportion of human-driven vehicles and autonomous vehicles,the "flow-density" relationship of heterogeneous traffic flow on single-lane roads is obtained.Based on the traditional signalized intersection capacity calculation model,the revised calculation model of capacity in mixed environment is derived,which is the revision of basic saturated flow rate under different proportion of autonomous vehicles.Theoretical derivation and SUMO simulation also show that: for single-lane road autonomous vehicles can greatly improve the traffic efficiency,when all ACC vehicles on the road,its maximum traffic capacity is 1.48 times that of artificial vehicles.When the road is full of CACC vehicles,its maximum capacity is 2.42 times that of manual vehicles.The mixing of autonomous vehicles will also improve the traffic capacity of the intersection,but the improvement degree is small,mainly restricted by the intersection signal control.Finally,in view of the new characteristics of roads and intersections in the mixed environment,based on the traditional queue balancing control strategy,the intersection queue balancing control strategy in the mixed environment is proposed,and the chaotic mapping optimization particle swarm optimization algorithm is proposed to solve the queue balancing objective function,and the experimental analysis is carried out by SUMO simulation.The queue length of vehicles under four conditions,namely fixed signal timing scheme under manual driving vehicle,fixed signal timing scheme under mixed environment,queue length balancing control scheme under manual driving vehicle and queue length balancing control scheme under mixed environment,were respectively compared.The results showed that: The increase of the proportion of autonomous vehicles can greatly optimize the control effect of the queue balancing control strategy while delaying the formation of the queue.Through the analysis of the average delay of vehicles,it is found that the increase of the proportion of autonomous vehicles under the queue balanced control strategy can effectively reduce the average delay of vehicles and further improve the traffic capacity of intersections.