A Study On Control Of Urban Traffic Trunk Signal Optimization

With the fast development of socio-economic level and the acceleration of the car process, the problems, such as traffic jam, environment pollution, become more serious, which has become an important restricting factor in urban economic development. How to relieve traffic jam, improve the efficiency of urban road have become a moment of concern problem by engineers and researchers.Because of the randomness of urban transport and uncertainty of the traffic flow, the traditional traffic control methods have not achieved good effect in urban traffic signal control system. So the intelligent control method has become one of the principal means in traffic signal control research field. This paper, fuzzy control theory combined with genetic algorithm applied to traffic signal control in order to optimize cycle and phase variables, the intersection signal timing optimization program can improve the green light utilization and reduce vehicle average delay. The method has demonstrated practical significance on the urban traffic control development.The paper regards an isolated intersection as research object, the simulation results the fuzzy control scheme indicate the effectiveness comparing with timing control, sensor control. After analysis of the current characteristics of classical two-stage fuzzy control algorithm, to improve its lack, this paper focuses on the control problem of four-phase scheme of main road, and describes a cooperative signal control scheme, and designs a modified two-stage fuzzy control algorithm. The algorithm amended the issues that the classical two-stage fuzzy control algorithm in the green light delay does not consider issues such as cycle time remaining. The simulations indicate the method can effectively reduce the vehicle average delay. Meanwhile, to solve the artificial membership functions rationality of the fuzzy controller, the Genetic Algorithm optimization is used to real-timely optimize membership functions. Under different traffic conditions, the simulation results are compared with other control schemes, the control effect has been further improved and verified the optimization algorithm.