Study On Urban Dynamic Traffic Signal Optimization Control Based On The Physical Queue

The emergence of Intelligent Transportation System (ITS) provides a new way to solvetraffic congestion, improve traffic conditions and increase road traffic ability.The advancedtraffic management system (ATMS) is a core part of the ITS. The urban traffic signal controlsystem is a critical component of the ATMS and an effective way to solve the traffic problem,and the effective traffic signal control depends on accurate modeling of dynamiccharacteristics of traffic flow. Supported by the National Natural Science Foundation ofChina of physical-queue dynamic traffic assignment research with multiple vehicle types, thepaper, based on physical-queue model of traffic flow to capture the dynamic characteristics oftraffic flow, researched the urban dynamic traffic signal optimization control problems in twodifferent ways: One is not to consider the impact of the traffic network user’s route choicebehavior on traffic flow patterns and the other is to consider the impact of network user’sroute choice behavior on traffic flow patterns. With a given traffic flow patterns as aprecondition, the signal optimal control model and optimization methods for optimal signalcontrol strategy are developed by single-level optimization strategy.With a dynamic trafficflow patterns, a combined model of dynamic traffic signal control with dynamic trafficassignment and algorithm are developed by bi-level programming strategy to determine theoptimal signal control strategies and optimization of path flow.The main achievements of the dissertation are as follows.(1)The traffic signal optimization controls of single intersection are studied under trafficdemand uncertainty. The operation of traffic flow conditions, to be reflected the dynamiccharacteristics of traffic flow, is described by using CTM (cell transmission model, CTM).Atraffic signal optimization control model based on CTM is presented, considering trafficdemand uncertainty.To optimize the traffic signal settings,the genetic algorithm is used. Thesimulation results show that the model can effectively reflect the impact of the fluctuation oftraffic flow on the signal timing and vehicle delay calculation in a variety of traffic conditions,and the optimization of the intersection traffic signal settings is realized.(2)The multi-objective optimization problems of urban multi-intersection traffic signal control are studied. The total delay, fuel consumption and vehicle exhaust emission of roadnetwork were taken as optimization objectives, and their calculation formulas were derivedbased on CTM, the operation of traffic flow conditions is described by using CTM.Amulti-objective optimization model based on CTM for urban traffic signal control ispresented. The solution of traffic signal is optimized by using multi-objective geneticalgorithm. Simulation result shows that the coordination control of traffic signals can beachieved, and the optimization settings of intersection traffic signals are realized under mild,moderate and heavy traffic flow conditions.(3)The dynamic traffic signal optimization problems in networks combined withmulti-user dynamic traffic assignment are studied. The dynamic characteristics of traffic flowsuch as spillback queue are captured by using physical-queue dynamic network traffic flowmodel.Considering a multi-user route choice behavior in the time-varying demand andcontrol strategies, based on dynamic traffic flow patterns, a generalized bi-level programmingmodel for dynamic traffic signal optimization in networks with multi-user dynamic trafficassignment is presented. The solution of traffic signal is optimized by using intelligentoptimization algorithm. The simulation results show the optimization of the intersectiontraffic signal settings and network traffic flow can be realized.(4)A generalized bi-level programming model for dynamic traffic signal optimization innetworks with multiple vehicle types stochastic dynamic traffic assignment is presented basedon a physical-queue dynamic network model with multiple vehicle types. The upper-levelproblem optimizes signal settings, while the lower-level represents a multiple vehicle typesstochastic dynamic traffic assignment problem,which sets network traffic flow.The simulationresults show the reasonability and validity of the model.