Study On The Capacity And Operational Efficiency Of Signal-controlled Intersections Under Mixed Traffic Of Grid-connected Vehicles

The state of the operation of the road traffic system is destined to change with the advancement of Vehicle to Everything(V2X)technology and intelligent connected vehicles.We will use the traffic application scenario of mixed traffic of intelligent connected vehicles and connected human-driven vehicles in V2 X environment as the research object in order to investigate the signal-controlled intersection capacity in the mixed state of network-connected vehicles.We will look into the intersection capacity influencing factors,intersection capacity measurement model,and intersection operation efficiency of dynamic signal-controlled in the mixed state.The study’s findings will have a significant impact on how intersections are planned,managed,and later controlled in the setting of mixed traffic flow.Firstly,the primary research contents are recognized and the current status of research on technologies linked to networked vehicles,mixed traffic flow,and intersection capacity is summarized.The analysis demonstrates that saturation flow rate and effective green signal ratio are the key parameters for calculating the capacity of signal-controlled intersections,and both of them will change in a V2 X environment.This is accomplished by combining theories related to intersection capacity concept and calculation methods,as well as cooperative control of intelligent connected vehicles at intersections.Four potential types of following in the mixed state of human-driven vehicles and intelligent connected vehicles are discussed in order to analyze the factors affecting the saturation flow rate at intersections.Additionally,an analytical model of saturation flow rate in the mixed state of intelligent connected vehicles and human-driven driven vehicles is established based on a probabilistic model,and the parameters in the model are analyzed by numerical simulation.It is demonstrated that the intersection capacity can be impacted by the penetration rate,vehicle speed,predicted headway time distance,following type,and effective green signal ratio of the intelligent networked vehicles in the context of mixed traffic flow.Secondly,a model is built to assess the dynamic signal-controlled intersection’s capacity and associated factors in the presence of mixed traffic.In order to determine the dynamic signal control intersection capacity using the saturation flow rate approach,the dynamic signal control mechanism and the calculation method of dynamic signal control timing parameters under the connected vehicle environment are investigated.Simulation is used to determine the saturation flow rate of straight and left-turn lanes under various permeabilities,and Matlab is then used to fit the simulation results with one-element nonlinear regression to obtain the saturation flow rate measurement model under various permeabilities.The effective green signal ratio measurement model of dynamic signal control under mixed traffic is modeled based on the two objectives of maximum capacity and minimum delay.And the weight coefficients in the model are assigned using the fuzzy matrix method.Following assignment,a genetic algorithm is used to resolve the effective green signal ratio.Finally,SUMO simulation is used to validate the measurement model discussed above,and the operating effectiveness of intelligent connected vehicles at various penetration rates is examined.The simulation environment is generated using SUMO simulation after data on intersection canalization,traffic flow,saturation headway,and driving speed are obtained through traffic survey.In the simulation,the Cooperative Adaptive Cruise Control(CACC)car following model is chosen for the intelligent connected vehicles and the Wiedemann car following model is chosen for human-driven vehicles.The simulation is used to determine the saturation flow rates of the straight and left-turn lanes under various permeabilities.The intersection capacity error calculated by the measurement model is within 4% at any permeability,according to the error analysis between the simulation results and the measurement results,showing that the proposed measurement model has some reference importance.The average queue length and average delay at intersections decrease as penetration of intelligent connected vehicles increases,with negligible changes at 0%～20%penetration;from 30% to 70% penetration,there is a significant decrease,with the greatest decrease occurring at 50%～60% penetration;from 70% onward,there is no discernible change in the average queue length and average delay at intersections.From a fully manually driven vehicle to a 100% penetration of intelligent connected vehicles,the average green light time was decreased from 146 seconds to 66.5 seconds,a 54.4% reduction;the average queue length was decreased from 39 meters to 7.5 meters,an 80.7% reduction;and the average delay was decreased from 58.8 seconds to 20.7 seconds,a 65% reduction.According to the study’s findings,an environment with intelligent connected vehicles can considerably increase the efficiency of intersection operation.