Traffic signal control for isolated intersections based on fuzzy neural network and genetic algorithm

Authors:
Tahere Royani;Javad Haddadnia;Mohammad Alipoor
Affiliations:
Department of Electrical Engineering, Tarbiat Moallem University of Sabzevar, Sabzevar, Iran;Department of Electrical Engineering, Tarbiat Moallem University of Sabzevar, Sabzevar, Iran;Department of Electrical Engineering, Tarbiat Moallem University of Sabzevar, Sabzevar, Iran
Venue:
ISCGAV'10 Proceedings of the 10th WSEAS international conference on Signal processing, computational geometry and artificial vision
Year:
2010

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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 performances and adaptability.