This paper presents a Simulation of Fuzzy Traffic Controller design for controlling Green Light time for effective traffic flow. Intelligent Traffic controllers are required these days to adjust to a situation of ever increasing traffic. Artificial Intelligence technique such as neuro-fuzzy systems, fixed time embedded controllers, etc. are available to handle the traffic related problems. But Adaptive traffic signal controller based on Fuzzy Inference System used in this project provides smart solutions for efficient traffic control. This system reflects two fundamental aspects of traffic responsive signal control- the observation of on-going traffic situation around the intersection, and the control of the traffic signals in a manner appropriate to the observed situation. In traffic signal control system, detection of traffic variables at intersection is very important and is the basic input data to determine signal timing. The controller is developed based on traffic density and traffic flow rate. This FIS module is developed in SIMULINK environment of MATLB tool which has achieved the satisfactory results for traffic signal control. The “Adaptive Traffic Signal Controller based on Fuzzy Inference system” is capable of taking decision to reduce delays at intersection.

1. Lu Conglin, Wei Wu, IEEE Member, Tan Yuejin, “Traffic Variable Estimation and Traffic Signal Control Based on Soft Computation”, IEEE Intelligent Transportation Systems Conference, Washington, D. C., USA, October 3-6,2004.
2. Shilpa S. Chavan (Walke), Dr. R. S. Deshpande, J. G. Rana, “Design of Intelligent Traffic Light controller using Embedded System”, International conference on Emerging Trends in Engg. & Tech., ICETET-09.
3. Yan Li and Xiaoping FAN, ”Design of Signal Controllers for Urban Intersections Based on Fuzzy Logic and Weightings” in proc. IEEE Conf. July 23-7, 2003.
4. Dipti Srinivasan, Min Chee Choy, and Ruey Long Cheu, ”Neural Networks for Real-Time Traffic Signal Control”, IEEE Transaction on Intelligent Transportation Systems,Vol. 7, No. 3, September 2006.
5. C. T. Wannige and D.U.J. Sonnadara, “Traffic Signal Control Based on Adaptive Neuro-Fuzzy Inference” 4th international conference on information and automation for sustainability, ICIAFS, 2008.
6. Md. Shabiul Islam, Masuri Othman, Md. Anwarul Azim, M. S. Bhuyan, M. Saukat Jahan, H. R. Siddiquei, “Design and Implementation of an Intelligent Fuzzy VLSI Chip for Traffic Control Application”, IEEE region conference TENCON, 2006.
7. WM El-Medany and MR Hussain, “FPGA based advanced real traffic light controller system design”, IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDACS, 2007, Dortmund, Germany, 6-8 September 2007.
8. N. Y. Hamisi, N. H. Mvungi, D. A. Mfinanga, B. M. M. Mwinyiwiwa, “Prospects of Pre-timed Arterial Traffic Control Systems in City Roads of a typical Developing Country: A case Study of Dar es Salaam City”, 2nd international conference on adaptive science and technology ICAST, December 2009.
9. Yi Hu, Andrew Chiou and Qinglong Han, “Hierarchical Fuzzy Logic Control for Multiphase Traffic Intersection Using Evolutionary Algorithms”, IEEE International Conference on Industrial Technology, ICIT, 2009.
10. Xing Zheng and Lianyu Chu, “Optimal Parameter Settings for Adaptive Traffic-Actuated Signal Control”, 11th International IEEE Conference on International Transportation Systems, ITSC, 2008.
11. Liu Yang and Chen Xian Feng, “Design of Traffic Lights Controlling System Based on PLC and Configuration Technology”, International Conference on Multimedia Information Networking and security, MINES, 2009.
12. Lawrence Y. Deng, Nick C. Tang, Dong-liang Lee, Chin Thin Wang and Ming Chih Lu, “Vision Based Adaptive Traffic Signal Control System development”, Proceedings 19th International Conference on Advanced Information Networking and Applications, AINA, Volume 2, 2005.
13. Azura Che Soh/Lai Guan Rhung and Haslina Md. Sarkan, “MATLAB Simulation of Fuzzy Traffic Controller for Multilane Isolated Intersection“.
14. P. Chakraborty and A. Das (2003), “Principles of transportation engineering”. Prentice Hall of India Private Limited New Delhi. EEE,
15. Garber and Hoel ,”Traffic and highway engineering”.

Fuzzy Inference System Intelligent Delays at Intersection Simulink Density Flow rate