A Study On Intelligent Control Technique For Urban Traffic

Intelligent control technique for urban roadway traffic is an important topic in control domain and traffic engineering domain. With the fast development of artificial intelligence (AI), automatic control, computer and communication techniques, many traffic flow modeling and analysis by synthesis method have been found. New theory and research achievement have been published in resent year. Their applications in engineering have been shown its tremendous powers and huge potentials.In this dissertation we mainly study and analyze the new models and control arithmetic to urban traffic system. We make use of artificial neural network (ANN) to model urban traffic system which is a heavily nonlinear, stochastic, time-variant and uncertain system. Moreover we design the structure and learning arithmetic of ANN. We also design some advanced intelligent control arithmetic to urban traffic by the use of ANN, fuzzy logic theory and large scale system theory. Analysis and simulation show that this arithmetices are more robust, self-adaptive and self-learning, and can solve the urban traffic problem more effectively than conventional traffic control methods.The main contributions of the dissertation are as follows:1. We make an overview on the urban traffic control system in detail, including generation, development and the last achievement, and make a discussion on some difficulty and problem in the field including theory analysis and application. We also introduce the future research direction and development policy on intelligent traffic system in our country.2. We design a novel multi-phase fuzzy traffic control arithmetic for single intersection. Firstly, we describe the model of single intersection. According to the manned decision process, we also introduce the fuzzy logic controllerbased phase sequencer. The arithmetic can deal with more stochastic and more uncertain traffic flow.3. We use the principle of decomposition-coordination of large-scale systems, fuzzy theory and ANN technique to solve the real time arterial coordinated control problem. The urban traffic trunk is regarded as a large system, and the subsystems are the intersections in the trunk. Then we desipi a two-level coordinated fuzzy control method and use ANN to implement the fuzzification, fuzzy inference and defuzzification. The arithmetic hoMs the strongly self-adapt, self-learning and fault-tolerant function.4. We use the fuzzy neural networks (FNN) theory to solve the real time region traffic distributed control problem. At each intersection, we set an intelligent controller which dynamicly manages phase sequences, phase switch, signal cycle and split. Because ANN is adopted to implement fuzzy relation, the system is excellent and strongly robust.5. We use ANN and fuzzy theory to study urban expressway model and multilayer intelligent control system. We develop an extended version of the METANET traffic flow model to describe the evolution of the traffic flows in the urban expressway. Then we simplify the model to suit for traffic control approach and describe it by ANN. We also propose a multilayer intelligent control structure to solve the problem of traditional control systems in conjunction with the characteristics of traffic systems.6. We give the urban region and expressway integrated model use FNN theory to solve the real-time region and expressway traffic distributed control problem. The presented method can make the expressway unblocked and the average vehicle delay time shortest.7. We discuss the new highway model and intelligent control method. We model the urban highway traffic flow and design a multi-variables control strategy with both the on-ramp control and the road speed control by the ANN technique. The developed adaptive neural controller is to control the traffic density and force it to follow a desired one. This controller is aservo system that states and controls can be followed.8. We present the architecture of the urban traffic...