Study On Intelligent Control Strategy For Urban Traffic Signal System

The aggravation of urban traffic congestion becomes a social issue for the development of cities over the world, as it causes increased vehicle delay, frequent traffic accidents, and worsening environment. ITS (Intelligent Transportation System) is one importation solution to traffic congestion. Intelligent control of urban traffic signal is not only a hot issue of both automatic control and traffic engineering, but an important element of ITS. As a complex system of heavy nonlinearity, randomness, time-variability, urban traffic system is beyond the capability of traditional control method which is based on precise mathematical model, so increased importance is attached to artificial-intelligence method.In this thesis, in order to relieve traffic congestion, in-depth study on intelligent control of urban traffic signal is carried out based on the fusion of fuzzy logic, genetic algorithm, and artificial neural network.The main achievements in this thesis include:①A traffic intensity-based control algorithm for isolated intersections is presented.This algorithm not only takes into account and the number of vehicles between the two detectors, but also the flow ratio, red signal duration. It breaks through the limitation that the inputs of fuzzy controller only include the number of vehicles between the two detectors. It objectively reflects the urgency degree of traffic flow in each phase. Simulation result shows that this algorithm is more effective in reducing the average delay.②A coordination control algorithm is presented for two adjacent intersections.In this algorithm, traffic volume in the link of two adjacent intersections is the coordination variable. The next green phase is determined according to the traffic condition of each red phase. Fuzzy inference is carried out to determine whether to stop the current green phase. Simulation result shows that this algorithm is effective in reducing the average delay.③A hierarchical coordination control algorithm for arterials is put forward.According to the concept of decomposition-coordination, traffic volume in the link of two adjacent intersections is the coordination variable. The coordination element modifies the extension of green signal according to coordination variable. This algorithm breaks through the limitation that all intersections are controlled with a same cycle which is applied in green-wave method. It improves the adaptability and real-time operation. Simulation result demonstrates that this algorithm is more effective in reducing vehicle delay.④A distributed coordination control algorithm is presented for area traffic.According to the distributed control, it's unnecessary to determine a common signal cycle for all intersections. The coordination control is carried out according to the traffic volume in the link of two adjacent intersections and the traffic condition of each intersection. This algorithm improves the adaptability and real-time operation.⑤A control-orientated prediction algorithm for short-term traffic flow is presented.This algorithm takes into account the temporal and spatial relevancy of traffic flow, with mean-square error being the performance index. Genetic algorithm is applied to optimizing the initial weight and threshold. The prediction result indicates that this method outperforms the traditional BP neural networks in precision.In short, an in-depth study for urban traffic signal control is carried out by sufficiently synthesized fuzzy logic, GA and artificial neural network. The control adaptability and real-time property are both improved and average delay is reduced.