Development of Intelligent Traffic Light System Based On Congestion Estimation Using Fuzzy Logic

Vehicular traffic is the major problem which every country faces because of the increase in number of vehicles throughout the world, especially in large urban areas. In a conventional traffic light controller, the traffic lights change at fixed time. It does not provide an optimal solution. Many traffic light controllers implemented in current practice, are based on the 'time-of-the-day' scheme, which uses a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. A traffic light controller based on fuzzy logic can be used to optimize the control of unprecedented traffic volumes such as over saturated or unusual load conditions. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow. The efficiency of traffic flow through an intersection depends on the phases, sequence and the timing of the traffic signals installed to minimize the wait time of the vehicle in each queue. Fuzzy optimization deals with finding the values of input parameters of a complex simulated system which results in desired output. Fuzzy logic controller is used to execute fuzzy logic inference rules from a fuzzy rule base in determining the congestion parameters, getting the warning information and the appropriate action. This paper, describe the design and implementation of an intelligent traffic light system (ITLS) based on congestion estimation using fuzzy logic. In an intelligent traffic light system (ITLS) number of vehicles in each lane is measured using sensors. At the end of each phase these numbers are used as inputs to fuzzy controller. Fuzzy controller calculates the duration of green light as per the traffic situation. To simulate the situation of an isolated traffic junction based on congestion estimation, we use MATLAB. The results obtained show an improvement in the overall outcome of traffic management as compared to the conventional traffic controller, marking great feasibility and practicality of the current model. Keyword: Simulation, Fuzzy logic, ITLS, Arrival, Queue, Right-Queue, and Extension time.