Research On Adaptive Traffic Management Driven By Real-time Information In The Internet Of Vehicles Environment

With the advancement of economic and continuous improvement of peoples living standard,vehicle ownership explosively grows.While facilitating people's travel,it also brings many problems,such as traffic congestion and environmental pollution.In addition,people have also put forward new requirements for driving safety and comfort.Relying on increasing road resources can no longer meet the growing travel demand.Intersections are often the bottleneck of road capacity.Traditional intersection management methods use timed traffic lights to manage intersections.This method cannot well adapt to the randomness and uncertainty of traffic flow.Therefore,intersection management has also become a research hotspot in modern traffic management research.A minimum comfortable and safety braking distance was proposed to ensure that the vehicle can brake comfortably and safely during the car following process.In a connected vehicle environment,information about the vehicle's travel is collected by sensors mounted on the vehicle and forwarded through communication between V2X(Vehicle to Everything),and the junction control unit can dynamically calculate the distance of the vehicle according to the actual driving status of the vehicle.Subsequently,it was proposed to manage the vehicles at the intersection,and to reduce the unnecessary waiting time of vehicles on the multi-directional lanes through the reasonable allocation of lanes to the vehicles at the intersection.Finally,an adaptive traffic signal lamp is proposed.Real-time information-driven method is adopted to dynamically adjust the cycle time of each phase according to the actual driving condition of vehicles.Finally,a complex intersection scenario was set up on the SUMO(Simulation of Urban MObility)traffic simulation platform to evaluate the method proposed in this paper,and compared with the existing classic methods and international research results.The experimental results show that the method proposed in this paper can effectively reduce the average waiting time and the waiting time of vehicles with longest waiting time.In addition,the distribution of waiting time of vehicles is counted to prove the effectiveness of the method proposed in this paper.