Abstract—In this paper a fuzzy neural network is applied for real time traffic signal control at an isolated intersection. The FNN has advantages of both fuzzy expert system (fuzzy reasoning) and artificial neural network (self-study). A traffic light controller based on fuzzy neural network can be used for optimum control of fluctuating traffic volumes such as oversaturated or unusual load condition. The objective is to improve the vehicular throughput and minimize delays. The rules of fuzzy logic controller are formulated by following the same protocols that a human operator would use to control the time intervals of the traffic light. For adjusting the parameters of FNN, genetic algorithm was used. Compared with traditional control methods for traffic signal, the proposed FNN algorithm shows better performance and adaptability.

Index Terms—Fuzzy neural network, traffic control, delay, genetic algorithm, performance.

T. Royani and J. Haddadnia are with Tarbiat Moallem University of Sabzevar, Electrical and Computer Engineering Faculty, Sabzevar, Iran(e-mail: tahere.royani@gmail.com; haddadnia@sttu.ac.ir).
M. Alipoor was with Tarbiat Moallem University of Sabzevar, Electrical and Computer Engineering Faculty, Sabzevar, Iran. He is now with Chalmers University of Technology, Sweden (e-mail: mbiomedical@yahoo.com).

Cite: T. Royani, J. Haddadnia, and M. Alipoor, "Control of Traffic Light in Isolated Intersections Using Fuzzy Neural Network and Genetic Algorithm," International Journal of Computer and Electrical Engineering vol. 5, no. 1, pp. 142-146, 2013.