INTELLI TRAFFIC MANAGEMENT SYSTEM

Urbanization and rapid population growth have significantly increased traffic congestion, pollution, and fuel consumption our metropolitan areas. Traditional traffic management systems are rigid which rely only on fixed signal timings, fail to adapt to real-time traffic conditions. This leading to inefficient traffic flow and prolonged delays. The advancement of Internet of Things (IoT) and Machine Learning (ML) provides a promising solution to these challenges. This paper presents a Intelli Traffic Management System (ITMS) that utilizes IoT sensors, AI-driven traffic analysis, and real-time data processing to optimize signal timings dynamically. The system employs YOLO v11 for vehicle detection, LSTM neural networks for congestion prediction, and a Priority Round Robin Algorithm for adaptive traffic signal control. These components work together to analyse live traffic conditions, adjust signal durations accordingly, and ensuring seamless urban mobility. The web-based integration allows instantaneous updates, enabling traffic the users to monitor congestion. Traffic administrators manually control intersections, and improve emergency response times. Additionally, by reducing idle time at junctions, the system contributes to fuel conservation, lower carbon emissions, and enhanced road efficiency. Through data-driven urban planning and intelligent decision-making, STMS represents a crucial step toward sustainable and eco-friendly smart cities. Keywords: Smart Traffic, IoT, Machine Learning, Urban Mobility, Sustainable Transportation, Adaptive Signal Control