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Efficient Way to Control Road Traffic Using Fuzzy Logic

I. Gobi, Dr. D. Vimal Kumar


Traffic through road travel makes congestion in roads causes’ major problem to road users. This is due to regular use of vehicles through the cities and waits in traffic signal for a long time which makes traffic impasse. To manage this problem many traffic control system have been developed. Due to increase in vehicles the traffic controlling demand are high. To control these traffic new techniques was proposed and named as Efficient Road Traffic Controller (ERTC) which efficiently reduces congestion in traffic signal. The research model will control the traffic by the adjustment of time and phase of the traffic lights by the situation of traffic intersection and controlled by indication to the applying model.

This paper gives a brief discussion of the procedures we adopted to develop an intelligent fuzzy control system for dealing with the road traffic congestion problem. Specialized node is used in the congestion road traffic which is known as Local Cognitive Node (LCN) implements the learning components and decision making. Fuzzy logic technology is used to develop the system with Cognitive sensor node where these nodes use learning mechanism to take decisions at LCN. The result of the Simulation of proposed system shows that problem of traffic congestion is efficiently reduced in the traffic network.


Wireless Sensor Networks, Traffic Congestion, Fuzzy Logic, Fuzzy Rules, Cognitive Node.

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Askerzade, I.N, Mustafa S.Mahmood “Design and Implementation of Intelligent Traffic Control by Using Fuzzy Logic”, Talk in 1st International Fuzzy Systems Symposium October 1-2, Ankara, pp.52- 59, 2009.

O. C. Akinyokun, Neuro-Fuzzy Expert System for Evaluation of Human Resources Performance. First Bank of Nigeria PLC Endowment Fund Lecture Series 1, Delivered at the Federal University of Technology, Akure, Nigeria, 2002.

L. GiYoung, J. Kang & Y. Hong, The optimization of traffic signal light using artificial intelligence. Proceedings of the 10th IEEE International Conference on Fuzzy Systems.Barisban Australia, 2001.

J. Niittymäki & M. Pursula, Signal Control using Fuzzy Logic, Fuzzy Sets and Systems, Vol. 116, 2000, pp. 11-22.

C. P. Pappis & E. H. Mamdani, A Fuzzy Logic Controller for a Traffic Junction, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-7, No. 10, 1977, pp. 707-717.

L.A. Zadeh, Fuzzy Sets, Information and Control, 8, 1965, 338-353.

L. Zadeh Applied soft computing- foreword. Appl. Soft Comput. 1: 1-2, 2001.

S. Horikawa, T. Furuhashi, & Y. Uchikawa, On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm,” IEEE Transactions on Neural Networks, 3, 1992, 801- 806

G. K. Mann & R.G. Gosine, “Adaptive hierarchical tuning of fuzzy controllers,” Expert Systems, 19(1), 34-45, 2002.

J. Chen, & Y. Xi, Nonlinear System Modeling by Competitive Learning and Adaptive Fuzzy Inference System,” IEEE Transactions on Systems, Man, and Cybernetics,Part C: Applications and Reviews,vol. 28, no. 2, 1998, pp. 231-238.

K. Tan, M. Khalid & R. Yusof, Intelligent traffic lights control by fuzzy logic. Malaysian Journal of Computer Science, 9(2): 29-35, 1996.

U. C. Osigwe, F. O. Oladipo, E. A. Onibere, Design and Simulation of an Intelligent Traffic Control System. International Journal of Advances in Engineering & Technology Vol. 1, Issue 5, 2011, pp. 47-57.

Avhad Kalyani B. "Congestion Control in Wireless Sensor Network-A Survey”. International Journal of Computer & organization Trends (IJCOT), V2 (4):99-101 2012.ISSN Published by Seventh Sense Research Group.

Pouya Bolourchi and Sener Uysal , Forest Fire Detection in Wireless Sensor Network Using Fuzzy Logic , 2013 fifth international conference on computational intelligence communication systems and networks.


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