Study Of Urban Traffic Signal Control System Based On Intelligent Computation

With the development of economy and advancement of urbanization, urban population and vehicle number is increasing year by year. Traffic congestion and jam has become a common problem to be faced by large and medium-sized cities in the world. And a series of social problems brought by traffic congestion also trouble them, such as traffic accidents, environment pollution, energy crisis and so on. Developing urban intelligent traffic control system is one of the important ways to solve contradictions between traffic demand and supply. Implementing intelligent control of urban traffic system is not only beneficial to improve transportation efficiency and enhance road traffic safety, but also relates to making full use of land source and energy, improving urban environment, developing national economy and social benefit.Urban traffic signal control based on intelligent computation is one of the important contents of urban traffic intelligent control, which is of significance to improve traffic capacity of urban road network and reduce delay of vehicles. Recently, most intersections in larger and major Chinese cities are signalized, and signal controllers mostly adopt fixed time signal control strategy. Otherwise, video detectors and induction detectors are placed on the mass of intersecitons on arterial roads to detect traffic flow volume. Based on the construction situations of urban transportation infrastructure facilities, the paper tries to establish a signal control system based on traffic pattern recognition. If traffic flow detectors are equipped for all of the intersections of control area, suitable signal timing plans can be called according to traffic pattern recognition based on traffic flow information detected by detectors. If detectors are not installed in part of intersections of control area, control time domain will be divided into a series of sections according to traffic pattern recognition based on temporal distribution of traffic flow, and corresponding signal timing plans will be invoked at different time. Signal timing plan library is prepared beforehand, and optimal algorithm combines arterial coordinated control with robust optimization of signal timing at single-point intersections. For the control area without detectors, the system employs fixed time signal control strategy with low implement and maintain cost of traffic controllers. Besides, the system has ability of enhancing capacity of intersections, improving efficiency of vehicles on signalized network, reducing delay and stopping rate, lowering energy consumption and exhaust emission. For the control area with detectors, the system utilizes plan-selection signal control system, which can make full use of hardware equipments and has ability of improving efficiency and stability of control plans.The main contents of this thesis are listed as follows:(1) Based on summarizing and reviewing research fruits on traffic flow model of urban signalized road network, a traffic flow model of signalized road network with wide applications is proposed based on cellular automaton theory. A one-dimensional cellular automaton model with improved open boundary conditions is used to simulate the traffic flow on arterial roads with coordinated control system. The model employs difference equations to describe dynamic behaviors of vehicles. The restriction on regularly spaced distribution of traffic signal lamps can be eliminated. Furthermore, the split on every intersection can be chosen according to traffic flow fluctuations. The offset between adjacent intersections can be adjusted by green wave control. Matlab is employed to simulate this model to analyze the impacts on mean velocity, density and volume of arterial traffic flow by the flow volume on the artery and the turning flow volumes from branches. Based on the results of simulation, a series of proposals for improving the arterial traffic situations are put forward, which is a prerequisite for constructing urban traffic signal timing library.(2) In consideration of the placement situations of urban signal controllers and detectors, a framework of economical and effective urban traffic signal control system is proposed. The paper analyzes basic principle, structure and functional modules of the system, and then explains concepts, principle and applied scope of intelligent computation methods such as rough set theory, fuzzy neural network, genetic algorithm, and cell transmission model, which are used to construct the system. (3) Based on rough set theory and fuzzy neural network, a rough fuzzy neural network model is proposed to realize traffic pattern recognition of intersections. The model is comprised of two stages. At the first stage, traffic parameters are reduced based on rough set theory to obtain the least reduction of attribute set which can describe traffic characteristics. At the second stage, traffic pattern recognition model is built based on fuzzy neural network using reduction parameters above. The model provides necessary theoretical basis for data collection, analysis and processing of traffic parameters, and it provides technical support for traffic pattern recognition, and it is prerequisite to establish traffic signal control system.(4) The idea of robust control for intersections is proposed to remedy the disadvantage of fixed-time signal control that could not be suitable for large fluctuations of traffic flow. The sub-objective function is added to traditional optimal objective function. The robust objective function which strengths stability of signal control is to minimize standard deviation of vehicle delay. Based on simulation and analysis of intersections under various traffic conditions, the study establishes the relationship between sub-target weights and flow fluctuating ranges. Then the paper builds a multi-objective optimization model to optimize cycle length and splits of single-point intersections.(5) Coordinated control system of arterial roads is constructed to optimize offset between adjacent intersections. The model simulates traffic flow on urban signalized arterial road by cell transmission model, and constructs mathematic models of delay, stopping rate and traffic volume based on the platform. The paper proposes a optimization model to optimize the offset between adjacent intersections of coordinated control system. Its objective function is to minimize total delay and stopping rate and to maximize traffic capacity of arterial road. And its constraint condition is offset constraint. Genetic algorithm is executed by Matlab to solve the model. And the experiment results show that the model effectively reduces the delay and stopping rate of vehicles running on arterial road and largely improves traffic capacity of artery.(6) Combing single-point signal robust control model with coordinated control model of arterial roads, traffic signal control library is built to optimize signal timing of intersections, including cycle length, split and offset. The model provides thoughts for area signal control, considering traffic flow characteristics and the effect of queue length of upstream intersections on signal timing of downstream intersections and adjustment effect of signal control to traffic flow. Finally, the model is applied to area signal control. And combined with real road network, computerized simulation is carried out. The results show that the model not only effectively reduces average delay and stopping rate of vehicles running on arterial road and largely improves traffic capacity of arterial road, but also reduces the sensitivity of signal control for flow volatility.Finally, the paper summarizes innovations and shortcomings, and proposes future research priorities and orientations.