DEVELOP A "SMART TRAFFIC LIGHT" COMPUTER MODEL TO PREVENT TRAFFIC JAMS AT INTERSECTIONS

Abstract

Congestion at intersections is a critical problem affecting the mobility and efficiency of cities. Traditional traffic light systems often fail to adapt to real-time traffic conditions, leading to congestion, delays and increased fuel consumption. This thesis proposes the development of a computer model of a smart traffic light to prevent traffic jams at intersections and optimize traffic flow. Incorporating real-time data analysis, artificial intelligence algorithms, and adaptive control strategies, the model aims to dynamically adjust signal times and prioritize vehicles to alleviate congestion and improve overall traffic management consists of designing and evaluating a computer model of an intelligent traffic light capable of proactively managing traffic flow, reducing delays, and improving the efficiency of urban transportation systems. The model uses advanced technologies to optimize signal times based on real-time traffic data, minimize congestion and improve the overall passenger experience. Sensors to collect real-time data on vehicles, vehicle volume and intersection traffic, implement communication systems from cameras and vehicles to the infrastructure. Analyze collected data, predict traffic conditions and develop algorithms to optimize signal times to improve traffic flow. Dynamically adjust signal times, prioritize vehicles based on urgency and implement adaptive control strategies in the model to maximize traffic efficiency.[4]