Artificial Intelligence-Based Smart Traffic Control System

The fundamental aim of this research paper is to make Machine Learning-Based Smart Traffic Control System. The traffic light timer timing changes on detecting the traffic density count at each crossroad. Traffic congestion is most common problem in the major highly populated cities across the world, and it has made traveling very tough from one place to other. Traditional traffic lights run on the fixed timer concept assigned to each side of the road which can’t be changed as per changing vehicles density. In some situation lane with higher density demands longer green time as compared to the basic fixed time. The object or vehicles in the traffic signal are detected using cameras then processed into a simulator then its threshold is assumed on the basis of vehicle count in respect to each lane and compute the total number of vehicles present in the given area. After computing the total number of vehicles the system will acknowledge that which side the density of vehicles is high and based on the density the signals will be allotted for a particular side. Traffic mishaps or accidents are very common at overcast, rainy day, night when no street lights are available, foggy day and many others when there is minimum visibility. Traffic light control is one of the severe technical hazards of the Major cities in almost every country across the world. This is due to exponential rate of growth in the number of vehicles. In respect to minimize the time, a system has to be come up with the technology of artificial intelligence which makes a machine to think themselves. This modern developed technology will help the traffic light to switch the traffic signals from green to red based upon traffic density. This paper is related with the enhancement of traffic control system using machine learning which will be base on the different density on each lane.