Smart Traffic Management System with Real-Time Optimization Using AI and IoT

Rapid urbanization and the exponential growth of vehicles have led to severe traffic congestion, increased travel time, fuel consumption, and environmental pollution. Conventional traffic control systems, which operate on fixed time intervals, fail to adapt to dynamic traffic conditions, resulting in inefficient road utilization. To address these challenges, this paper proposes a Smart Traffic Management System that integrates Artificial Intelligence (AI) and Internet of Things (IoT) technologies for real-time traffic monitoring and optimization.The system utilizes IoT-enabled sensors, cameras, and connected devices to collect real-time traffic data such as vehicle density, flow rate, and congestion levels. This data is processed using AI-based algorithms, including machine learning and computer vision techniques, to analyze traffic patterns and make intelligent decisions. The system dynamically adjusts traffic signal timings, prioritizes emergency vehicles, and predicts future traffic conditions to minimize congestion and delays. This research highlights the potential of AI and IoT-based intelligent transportation systems as a key component of smart city infrastructure, enabling real-time decision-making and efficient traffic management.