Research On Distributed Optimal Control Strategy Of Urban Road Traffic System

With the development of economics and society progress,the number of vehicles on urban roads is increasing,and the contradiction between road supply and traffic demand is becoming increasingly prominent.Traffic congestion,environmental pollution and energy consumption caused by traffic congestion have become universal problems that have to be solved urgently in the world.It is one of the principal means to alleviate urban traffic congestion to study the problem of urban traffic optimal control.With the increasing scale of the city,the centralized traffic control method is not suitable for the optimization control of urban traffic system because of the heavy computing burden of the control center and the poor scalability of the system.It is an effective way to relieve urban traffic congestion by introducing theories and methods in related fields and exploring new strategies of urban traffic distributed optimal control.At the same time,based on the hierarchical and multi-granularity characteristics of urban road traffic system,this dissertation utilizes granularity computing theory to describe urban road traffic system in hierarchically and multi-granularity way,which can accurately express the state of traffic elements,and then realize the hierarchical and multi-granularity optimal control of urban road traffic system.It is a useful approach to alleviate urban traffic congestion.Under this background,the dissertation takes the distributed optimal control of urban road traffic system as the core problem.Firstly,aiming at the problem of micro-traffic behavior modeling,a data-driven car-following model is proposed,which describes the micro-traffic behavior model of vehicles.Then,the vehicle trajectory is reconstructed using the proposed vehicle trajectory reconstruction method based on rough set theory,which improves the accuracy of vehicle trajectory data.Then,aiming at the distributed optimal control problem of urban road traffic system,a back-pressure based routing algorithm in the field of communication is introduced,and a distributed intersection signal control method based on back-pressure algorithm is proposed,which considers the influence of downstream section capacity on intersection phase switching.Considering the intersection phase coordination,a back-pressure based distributed traffic cooperative control strategy is proposed.Finally,based on the S-rough set theory of granularity calculation,a hierarchical multi-granularity optimization control method based on backpressure algorithm is proposed,which improves the efficiency of the urban traffic system.The detailed research contents of this dissertation are as follows:(1)Data-driven micro-traffic behavior modeling.Starting from the behavior pattern and characteristics of traffic participants,the formation process and behavior characteristics of micro-traffic behavior are studied,which can provide necessary theoretical support for urban traffic optimal control.This dissertation studies the micro-car-following behavior of vehicles in urban road network,and proposes a data-driven car-following model to describe the time-space trajectory under the car-following condition,which provides the necessary research basis for the reconstruction of vehicle trajectory in the urban road network.(2)Vehicle trajectory reconstruction based on rough set theory.Vehicle trajectory is a kind of very important traffic data.Based on the trajectory data,the information needed for traffic system control and management can be extracted.Aiming at the problem of vehicle trajectory reconstruction,an online map matching strategy under the framework of distributed traffic data management is designed to achieve the real-time vehicle data collection and map matching.Then,a method of vehicle trajectory reconstruction based on rough set theory is proposed.The trajectory reconstruction rules are extracted using rough set theory,and the reasonable trajectory reconstruction algorithm is designed to reconstruct the vehicle trajectory.The compensation and reconstruction of the trajectory data can improve the accuracy of the trajectory data,and provide the necessary data basis for urban traffic optimization control.(3)The distributed intersection signals control based on back-pressure algorithm.On the basis of microscopic traffic behavior modeling and vehicle trajectory reconstruction,the back-pressure routing algorithm in wireless communication field is introduced.Using the vehicle queue length,the pressure of intersection phases is calculated.The penalty function is utilized to balance the influence of downstream section capacity and queue length of upstream and downstream sections on phase pressure.A distributed intersection signal control method based on back-pressure algorithm is proposed.The proposed control method can improve the efficiency of the traffic system.(4)The distributed cooperative optimal traffic control based on back-pressure algorithm.Aiming at the problem of traffic signals coordinated control,the influence of downstream intersection phase state on pressure calculation is taken into account when calculating intersection phase pressure.CBBA(Consensus-Based Bundle Algorithms)is used to solve the conflict problem of multi-intersection phase switching.A distributed traffic cooperative control strategy based on back pressure algorithm is proposed.(5)The hierarchical multi-granularity optimal control of urban road traffic system.To achieve the optimal control for different traffic participants,the S-rough set theory in granularity calculation is used to characterize the urban road traffic system in hierarchical multi-granularity way.A method of calculating intersection phase pressure based on the hierarchical multi-granularity traffic system is proposed,in which the influence of dynamic characteristics of traffic elements is considered.Combining with the back-pressure algorithm,a hierarchical multi-granularity optimization control method for urban road traffic system is proposed.