Study On Traffic Signal Control Method For Urban Single Intersection

In recent years, with the rapid development of national economy and the acceleration of urbanization, traffic is becoming more and more crowded and the traffic accidents happen frequently. Traditional control methods can not effectively solve the increasingly serious traffic problems. Therefore, it needs some the latest technologies and means to fundamentally solve the traffic problems, and the emergence of intelligent transport system provides the possibility to solve this problem.The thesis takes the signal control of urban single intersection as the research object, some intelligent control methods such as fuzzy logic, neural network and particle swarm optimization algorithm are studied to alleviate the city traffic congestion and improve the efficiency of transportation.The classical fuzzy control on urban multi-phase single intersection is studied. Based on the comprehensive consideration of the characteristics of urban traffic flow, a fuzzy control strategy is presented in which the phase sequence can be optimized. The strategy takes the average delay of vehicles at the intersection as the index for evaluating the performance of signal control. In this strategy, various factors such as the queue length, the waiting time as well as the average arrival rate of vehicles are considered to determine the switching sequence of phase and distribution of green light time. The simulation results verify the effectiveness of the strategy.Aiming at the existing defects of the phase sequence-changeable fuzzy controller, a fuzzy control method is designed based on particle swarm optimization. Because the membership functions and fuzzy rules of the phase sequence-changeable fuzzy controller are designed based on the experience of experts, it has considerable subjectivity and arbitrariness. In this paper, the particle swarm optimization algorithm is used to optimize the membership functions and rules of the fuzzy controller so as to adapt to different traffic flow and realize the adaptive control of the traffic signal. The simulation results show that the new phase sequence-changeable fuzzy control method based on particle swarm optimization algorithm can efficiently reduce the average delay of vehicles at the intersection and further improve the control performance the fuzzy controller.From the perspective of traffic flow data, the fuzzy neural network is used for building the model of signal control for urban single intersection. Fuzzy neural network is organically constituted by fuzzy logic and neural network, which draw on their strengths and makes up for their shortcomings. It effectively improves the learning and expression ability of knowledge of the entire system. The simulation results show that its application in traffic signal control has more advantages.