Study On Optimization Control Algorithms Of Urban Traffic Signal

Traffic is very close to people's life. In most of large and secondary cities, the number of private vehicles is increasing rapidly with the stepwise enhancement of social economy and the continuous development of automotive industry. So more and more traffic congestion has appeared and caused more traveling time for passengers, this will lead to inferior social productivity, increasing noises and environmental pollution. Therefore, it is an urgent problem for traffic engineers and researchers to study how to alleviate traffic jams and improve the efficiency of transportation system effectively. Traditionally, physical expansion of traffic network is a main means to solve the congestion and jams. But this method will cause expensive cost and destroy urban environment, furthermore, the effect is not very obvious. So it is considered rarely now. The other way to alleviate traffic jams is to enhance the efficiency of traffic facilities and implement rational traffic control strategies. With the incessant improvement of data processing capability of computer and the ceaseless development of electronic techniques and advanced control theory, more and more emphasis is placed on the second method by traffic departments and researchers in the world. Without question, signal control at intersections is a primary component of traffic management and it plays an important role in the whole urban traffic. Thus, it possesses very important significance for urban traffic management to develop an accurate and effective signal control system.The research object is urban traffic signal and two aspects are included in this dissertation. The first aspect is study on the prediction model and method of short-time traffic flow based on chaos time series, which will provide necessary traffic data for signal controllers. The second one is study on the optimization control algorithms of urban traffic signal, which include the signal control algorithm at an isolated intersection and the signal coordinated control method at reginal intersections. Referring to correlative literatures widely, the dissertation finished two aspects of work above mentioned. Its contents accord with the current research direction of traffic control theory and are hot and difficult topics in the field of urban intelligent traffic. In the dissertation, the main contents and innovation points are as follows.(1) The prediction on short-time traffic flow is the foundation of urban traffic guidance and signal control. Good or bad prediction methods have direct impacts on the prediction results of traffic flow. Hence it will affect the precision of traffic signal control. In the past study, middle-term and long-term prediction on traffic flow have made better study outcomes, but the study about the prediction on short-time traffic flow(usually predicting traffic flow in the coming five to fifteen minutes) has not gained satisfactory results. The Chapter 3 in this dissertation does research on how to apply chaos theory to the prediction on short-time traffic flow. Firstly, it analyzes the chaos characteristics of traffic flow, and proves that chaos characteristics exist in the time series of short-time traffic flow based on the correlation dimension and maximum of Lyapunov exponents. Then a prediction model of short-time traffic flow is built based on chaos time series. By using the adding-weight local-region method, the prediction results are satisfactory. Therefore, the proposed method can provide more accurate traffic information for signal controllers.(2) Fuzzy logic has been developed to control traffic signal at an isolated intersection, which is used mainly for two aspects. One is fuzzy control of green extensive time for current phase and the other is optimization control of phases' sequence. Existing fuzzy control methods of phases' sequence include the skipping method and the auto-generating method. The former is to select next phase from preset phases according to current traffic flow. If there are few vehicles in the preset phase, then the system will skip it and select another phase where there are more queuing vehicles. The latter is to auto-generate the phase without preset phases based on the real-time traffic flow. These methods to optimize the phases' sequence tend to mislead passengers and drivers. They are suitable for those intersections with few passengers and non-motors. A fuzzy signal control algorithm for an isolated intersection is proposed based on the optimization of phases' sequence in the Chapter 4. Considering of passengers and non-motors, this algorithm can guarantee passengers and non-motor to pass the intersection safely by using minimal phase time. The method improves the existing traffic signal fuzzy control algorithms. Based on the real-time traffic data, this method responds to the variety of traffic flow dynamically by optimizing green extension and phases' sequence. The method can effectively enhance the utilization ratio of phase time and reduce average delay of all vehicles.(3) For traffic signal control at many intersections, rational coordinated control between neighboring intersections can improve the efficiency of traffic network. Most existing control methods were developed under the assumption that all intersections of the whole area were in the same traffic state and traffic signals were coordinated based on a common cycle length. In the fact, traffic volumes of all intersections are not always same or very close, so these methods will lead to more delay at some intersections with non-optimal cycles. Therefore, it is necessary to develop a method to partition the whole traffic area into several sub-areas dynamically according to the geographical characters of traffic network and real-time traffic data at intersections and optimize the signal control parameters based on the partition of sub-areas. Chapter 5 in the dissertation firstly searched existing traffic sub-area partition methods, under the foundation of previous research, and then proposed a signal optimization control model to minimize average delay of vehicles for traffic network based on partition of sub-areas. In the proposed model, HCM2000 delay model and Robertson's dispersion formula are used, hereby it is more accurate to estimate the performances of signal control plans.(4) Genetic algorithm is a stochastic and global search technique coming from an analogy with biology in nature, which was used in computing to find exact or approximate solutions of optimization problems widely. In order to gain the optimal solution of area traffic signal optimization model, Chapter 6 developed an optimization method based on genetic algorithm by using the Visual C++ software. Self-adapting strategies were used to calculate the crossover probability and the mutation probability.(5) Microscopic traffic simulation system CORSIM, as a without bias tool is used to estimate different signal control methods in the same traffic scenarios. Chapter 6 takes an arterial and a multi-arterial traffic network as simulation objects. Input signal control parameters, such as cycles, splits and offsets from genetic algorithm program and TRANSYT-7F into CORSIM respectively, then run CORSIM program and export the simulation results. The validity of proposed model can be verified by comparing the simulation results. It proved that the proposed optimization method for area traffic signals is prior to TRANSYT-7F.