Analysis of Intelligent Traffic Management System using Artificial Intelligence and Computer Vision

Abstract

The problem of traffic jam at the intersection of the streets in the city is an old issue, which results in wastage of time, use of more fuel, and more pollution of the environment. Traditional systems of traffic signals, which rely on a fixed schedule or fixed timer, tend to be inefficient at the peak time or responding to unknown changes in traffic flow. In order to address these shortcomings, the proposed Smart Traffic Light Control System will combine machine learning algorithms with vehicle detection systems in order to optimize signal timing dynamically. The vehicle detectors, real-time traffic analysis units, adaptive signal control functions, and coordination systems of the different intersections make up the system architecture. Cameras or radar devices are used as sensors to record continuous data of vehicle movements at intersections. Computer vision and detection algorithms are used to identify and track the vehicles and the information is processed as a measure of the current traffic density. Patterns are analyzed with a machine learning model and a prediction of the traffic demand is made and signal phases can be modified. Adaptive signal control algorithms dynamically allocate green time and favor lanes of greater demand and distribute green time evenly. Moreover, the coordination of the adjacent intersections ensures the minimization of the number of stops, minimization of delays, and an increase in the movement of traffic. The system helps to reduce greenhouse gas emissions by idle vehicles as well as enhancing road safety; it also makes the road system efficient and avoids the congestion problem before it occurs. The suggested framework is in line with the smart city programs, which excellently add to the sustainability, safety, and efficiency of transportation systems. The system is a major improvement when compared to the traditional method of traffic control as it combines artificial intelligence with intelligent traffic sensing. Its results demonstrate its possible impact in changing the movement of people in cities and provide scalable solutions to the problem of congestion and improving the lifestyles of people in cities.