Coordinated Signal Control Of Urban Main Corridor Considering Internet Of Vehicles

Urban main corridor carries the most vital traffic flow as the hub of the urban traffic system.It is easy to cause large-scale traffic congestion when the urban main corridor’s traffic infrastructure struggles to manage the high-intensity traffic load.In the past,traffic signal control was widely utilized to alleviate traffic congestion.However,due to the rapid growth of urban vehicles and the capacity limitation of traffic infrastructure,the traditional traffic signal control is also facing the situation of diminishing marginal utility in controlling congestion.With the iterative advancement of Internet of Vehicles technology,a new traffic signal management system that can use the Internet of Vehicles to sense vehicle information from the road network and perform dynamic optimization opens up a new avenue for urban traffic congestion control.Taking the urban main corridor as the research scenario,this thesis offers a signal coordination control framework in the context of the Internet of Vehicles to improve vehicle traffic efficiency.The Macroscopic Fundamental Diagram（MFD）is used in this thesis as a traffic state evaluation tool to control traffic flow entering the urban main corridor area at the macroscopic level.And then,this thesis combines the macroscopic control with the perimeter signal control on micro level,and realizes the fine control of the perimeter intersection of the main corridor area by using the feature that the traffic control center can obtain the information of vehicles under the environment of Internet of Vehicles.The characteristics of two-way communication between connected vehicles and the traffic control center are used in this thesis to direct vehicle speed in the urban main corridor.Simultaneously,the signal offset between the signal phases in the same direction of continuous intersections is set to optimize the vehicle traffic efficiency of the main corridor,based on road network conditions.The specific work contents are as follows:Firstly,this thesis uses model predictive control（MPC）as the macroscopic traffic flow control framework,and uses the traffic flow conservation equation to establish the macroscopic control model.In the control framework,this thesis uses the traffic flow conservation equation to anticipate the traffic state of the main urban corridor area and judges the traffic state using the MFD to trigger the perimeter control.In macroscopic control,MPC can obtain the optimal control parameters of each control step through genetic algorithm.Using this control parameter,the proportion of traffic flow entering the urban main corridor area can be limited,so that the urban main corridor area can achieve the desired better traffic state.Second,this thesis presents a microscopic signal control model based on the signal timing technique of perimeter intersection of urban main corridor area as the study goal.To predict the number of two types of vehicles passing through an intersection in the next control period,the model combines the control parameters obtained in the macroscopic control with the number of two types of vehicles in the mixed queue of buses and social vehicles at each intersection as perceived by the traffic control center.Based on the variable passenger capacity of buses and social vehicles,the model employs a differential evolution strategy to achieve the optimization goal of maximizing the total number of transferred passengers at the intersection group on perimeter.Then,a green wave band would be formed by adjusting the signal offset and vehicle would be induced to maintain a proper speed in the main corridor to prevent vehicles from stopping because they arrive at the intersection too early.So,these approaches can allow vehicles traveling in the main corridor to pass through continuous intersections as far as possible without stopping,thereby improving vehicle trafficability.Finally,to perform simulation experiments,the road network model is developed using the SUMO simulation platform,and the control program is implemented in Python.This thesis uses numerical simulation to verify the viability of the macroscopic control approach and connects the control program with the simulation program through the Tra CI（Traffic Control Interface）API to implement the control strategy presented in this thesis.The essential indicators acquired from the simulation experiment demonstrate that the control technique presented in this research may enhance the traffic status and efficiency of an urban main corridor.