| Urban Traffic Control (UTC) is an important part of ITS (Intelligent Transportation System), which adjusts the traffic flow, warns the drivers, and induces the vehicles to improve the security transport of human and freight. The purpose of UTC lies in taking full advantage of the existing transport capability, and improving the security, velocity and comfort of traffic flow. While the current traffic control systems cannot realize the above functions efficiently. Even in cities with advanced traffic management systems, it is not uncommon to intervene manually in the time plans, especially during peak hours. Also the laggard transport information management systems often lag the traffic control, produce more vehicle delays and fuel consumptions.This research firstly starts from the dynamic model of traffic flow, and analyzes the correlations between the macroscopic traffic flow theories and microscopic car-following models, and the deficiencies of themselves. The traffic flow model CTM (Cell-Transmission Model) uses the average values of the traffic flow parameters such as velocity, volume and density in macroscopic traffic to depict the vehicles in road network, so it can not represent the microscopic movement of vehicles in a given intersection at the certain time of a day. To the defects of CTM model, the dissertation uses the traffic wave theory and car-following theory to improve the current CTM model to adapt the dynamic characteristics of urban traffic flow. The improved CTM (named ICTM hereafter) is then developed to give the real-time traffic information in a signalized intersection in the traffic signal control. As the traffic model, the ICTM is a base for the following researches on signal control methods and traffic network signal control.On the basis of modeling a single intersection with the ICTM, the rules in fuzzy signal controller are optimized with Genetic Algorithms, and theoptimized fuzzy signal controller is used in signal timing in single intersection. Simulation results show that the optimized fuzzy signal controller can improve the system performance compared with the actuated control.To improve the system performance of the fuzzy controller, another method is used to design the adaptive fuzzy controller which is composed of a signal control system oriented to the control level and a signal controller oriented to the fuzzy rules regulation level. Also the fuzzy closed-loop relationship matrices are used to analyze the stability of the fuzzy signal system. The theoretic proofs show that the proposed adaptive fuzzy control system is stable in signal timing at a single signalized intersection. And the simulation shows that the adaptive controller with rule regulation decreases the vehicle delay time distinctly, and improve the system performance greatly.Furthermore, the ICTM model and the adaptive fuzzy controller are used in multi-intersections signal control. Based on the analysis of the correlation of traffic flows among the neighbor intersections in a road network, a weight fuzzy controller is designed to describe the correlations. The above adaptive fuzzy controller is improved to be used in the signal control in a road network. Then with the ICTM of the road network, the improved adaptive fuzzy controller is simulated.Finally, the above research about multi-intersections signal control is extended to the area signal coordination control. For a specific urban traffic road network in Changsha city, according to its traffic characteristics, the road network is divided into three sub-areas. With the idea of area coordination control, the real-time signal timings are generated to adapt the traffic flow by coordinating the contiguous intersections. Simulation results show the efficiencies of the ICTM model and the area coordination control.
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