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

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.

[1]  Yan Li,et al.  Design of signal controllers for urban intersections based on fuzzy logic and weightings , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[2]  S. Chiu,et al.  Adaptive traffic signal control using fuzzy logic , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[3]  Jarkko Niittymäki,et al.  Signal control using fuzzy logic , 2000, Fuzzy Sets Syst..

[4]  Hadi Kheyruri,et al.  Independent agents for urban traffic control problem with mobile-agent coordination , 2007 .

[5]  Hassan Bevrani,et al.  Adaptive Fuzzy Urban Traffic Flow Control Using a Cooperative Multi-Agent System based on Two Stage Fuzzy Clustering , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[6]  J Niittymaeki,et al.  THE ROLE OF FUZZY LOGIC PUBLIC TRANSPORT PRIORITY IN TRAFFIC SIGNAL CONTROL , 2001 .

[7]  Shinya Kikuchi,et al.  Application of Fuzzy Logic to the Control of a Pedestrian Crossing Signal , 1998 .

[8]  Lin Zhang,et al.  Signal Control for Oversaturated Intersections Using Fuzzy Logic , 2008 .

[9]  Yang Zhao-xia.  A Self-learning Traffic Signal Control Approach and Simulation , 2004 .

[10]  Van Den Bosch,et al.  A Test Bed for Multi-Agent Systems and Road Traffc Management , 2003 .

[11]  Visit Hirankitti,et al.  An Agent Approach for Intelligent Traffic-Light Control , 2007, First Asia International Conference on Modelling & Simulation (AMS'07).

[12]  Yin Han,et al.  Intersection Signal Control Approach Based on PSO and Simulation , 2008, 2008 Second International Conference on Genetic and Evolutionary Computing.

[13]  Ella Bingham.  Reinforcement learning in neurofuzzy traffic signal control , 2001, Eur. J. Oper. Res..

[14]  Mohamed B. Trabia,et al.  A two-stage fuzzy logic controller for traffic signals , 1999 .

[15]  Peter Stone,et al.  Multiagent traffic management: an improved intersection control mechanism , 2005, AAMAS '05.

[16]  Keith R. Bisset,et al.  Simulation of traffic flow and control using fuzzy and conventional methods , 1993 .

[17]  Lei Jia,et al.  An Intelligent Control Method for Urban Traffic Signal Based on Fuzzy Neural Network , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[18]  N. Nishizuka,et al.  Fuzzy Logic Phase Controller for Traffic Junctions in the One-way Arterial Road , 1984 .

[19]  E. Mamdani,et al.  FUZZY LOGIC CONTROLLER FOR A TRAFFIC JUNCTION , 1977 .

[20]  Dipti Srinivasan,et al.  Simultaneous perturbation stochastic approximation based neural networks for online learning , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[21]  Zhang Li-hao.  A Cooperative Fuzzy Control Method for Traffic Lights , 2001 .