Document Type : Research Paper


1 Islamic azad university Lahijan branch

2 Islamic Azad University Lahijan, Iran


Traffic flow systems are nonlinear and uncertain, so it is very difficult to find their optimal points. In traditional traffic control systems, the traffic lights of crossings change in a fixed time period that is not optimal. On the other hand, most proposed systems are sufficiently capable of coping with the uncertainties of traffic flow. To solve this problem, there is a need to develop expert systems that can manage the traffic flow of intersections in terms of its actual conditions in a normal and emergency situation. This paper introduces an optimal dynamic and smart eight-phase traffic light control system using fuzzy controllers in which the capability of fuzzy systems with human-like decision-making process is exploited. This algorithm reduces the waiting time of the vehicles in intersection queues and in traffic congestion by the strategy of optimal green light durations and dynamic phasing based on the lanes with heavier traffic and the critical conditions like the entrance of emergency vehicles into the intersection. At the same time, it keeps the simplicity and avoids computational complexity. This method is simulated for an isolated intersection based on the traffic feature and the random data input rate to determine the dynamic timing of the traffic light and optimal phases with the smart fuzzy method using MATLAB Software Package (Matlab, 2013). This approach assessed the proposed method of intersection traffic control in terms of efficiency and traffic density against the constant-timing system and four-phase system proposed by some researchers. The results show that the proposed method can be effective in improving intersection traffic control systems.


Main Subjects

Kelsey, R L., & Bisset, K R. (1993). Simulation of traffic flow and control using fuzzy and ‌ conventional methods. Fuzzy Logic and Control: Software and Hardware Applications, Prentice-Hall, M. Jamshidi (Ed.), Englewood Cliffs, NJ, p. 262–  278.
Kulkarni, G., & Waingankar, P. (2007). Fuzzy logic based traffic light controller. In Industrial and information systems. ICIIS 2007. International conference on (pp. 107–10).
Mario, Collotta, & Lucia, Lo Bello, & Giovanni, Pau (2015). A novel approach for dynamic traffic lights management based on Wireless Sensor Networks and multiple  fuzzy logic  controllers  Elsevier Ltd. All rights reserved.
Murat, Y. S., & Gedizlioglu, E. (2005). A fuzzy logic multi-phased signal control model for isolated junctions. Transportation Research Part C: Emerging Technologies, 13, 19–36. 
Niittymaki, J., & Pursula, M. (2000). Signal control using fuzzy logic. Fuzzy Sets and Systems, 116(1), 11–22.
Pappis, C., & Mamdani, E. H. (1977). A fuzzy logic controller for a trafc junction. IEEE Transactions on Systems, Man and Cybernetics, 7, 707–717. 10.1109/TSMC.1977.4309605.
 Shahraki, A., Shahraki, M., & Mosavi, M. (2013). Design and simulation of a fuzzy  controller for a busy intersection. In Computer applications technology (ICCAT), 2013 international conference on,1–6. 
 Trabia, M. B., Kaseko, M. S., & Ande, M. (1999). A two-stage fuzzy logic ontroller for traffic signals. Transportation Research Part C: Emerging Technologies, 7, 353–367.  URL:
  Wilamowski, B. (2012). Suitability of fuzzy systems and neural networks for industrial   applications. In Optimization of electrical and electronic equipment (OPTIM), 13th international conference on,1–7.
  Zaied, A. N. H., & Othman, W. A. (2011). Development of a fuzzy logic traffic system for isolated signalized intersections in the state of kuwait. Expert Systems with Applications, 38, 9434– 9441.
  Zou, F., Yang, B., & Cao, Y. (2009). Traffic light control for a single intersection based on wireless sensor network. In Electronic measurement instruments, 2009. ICEMI’09. 9th international conference on, 1-1040–1-1044.