Optimization of Smart Traffic Systems Using AI-Based Adaptive Signal Control

The optimality of the smart traffic systems is essential to solving the traffic-congestion problems in the modern cities, time-successful travels, and adequate transportation system utility. The orthodox way of managing traffic light control is not very efficient especially where the traffic flow dynamics in a certain area are unpredictable due to factors such as time. This research focuses on the development of the adaptive signal control system that will utilize artificial intelligence to learn traffic signal timings within the traffic environment for the control of the traffic flow. The method combines the concept of sensor networks, IoT technologies with reinforcement learning to predict the traffic intensities and subsequently optimize the signal phases for higher traffic throughput and low traffic delay. The information given in simulations proves that the proposed system is more effective than regular traffic control methods in decreasing congestion, increasing the traffic stream in roads, and increasing the usage of energy. The novelty of this work arises from its proposed system which could solve traffic issues by using a smarter system when it comes to the management of traffic in smart cities.