Research on Intelligent Transportation System Based on Fuzzy Neural Network

Abstract

In the face of the increasingly serious traffic issues, this paper takes the signal control of urban single intersection as the research object, and uses the fuzzy control system to study the urban traffic signal control. The algorithm comprehensively considers the three parameters, queue length, traffic flow and speed of the intersection vehicle to determine the green light distribution time of the traffic phase. Through theoretical analysis and actual needs find the shortcomings of the algorithm. Additional, membership function and fuzzy control rules are manually set by expert experience and historical data, and the whole system lacks self-learning ability. Therefore, the neural network algorithm with self-learning function will be incorporated into the fuzzy control system to form an intelligent traffic information system which think fuzzy neural network as the core parts. It draws on the strengths of both, makes up for their respective deficiencies, and effectively improves the ability of the entire system to learn and express knowledge. The simulation results show that it is greater advantageous to apply it to the signal control of the intersection.