AI-Based Intelligent Traffic Management System for Emergency Vehicle Prioritization Using YOLOv8 and Fuzzy Logic

In the era of rapidly evolving urban landscapes, traffic congestion has emerged as a persistent challenge that disrupts mobility, compromises environmental sustainability, and critically delays the movement of emergency services. Addressing these concerns, this study presents a smart and adaptive traffic management system that seamlessly integrates artificial intelligence, computer vision, and intelligent decision-making to transform conventional traffic control into a dynamic and responsive framework. The proposed system utilizes real-time visual data to accurately detect and classify vehicles, enabling continuous assessment of traffic conditions and efficient regulation of signal operations. A defining feature of this approach is its ability to intelligently prioritize emergency vehicles, ensuring swift and uninterrupted passage while significantly enhancing public safety. Furthermore, the incorporation of fuzzy logic introduces a flexible and context-aware control mechanism capable of handling the complexities and uncertainties of real-world traffic scenarios. By harmonizing real-time analysis with automated signal optimization, the system effectively reduces congestion, minimizes delays, improves fuel efficiency, and contributes to a cleaner environment. Overall, this work highlights the transformative potential of intelligent technologies in redefining urban traffic management, paving the way for smarter, safer, and more sustainable cities.