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

Sandesh Preeti, Ravi Khurana, Pardeep Arora and Nitin Khanna

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 3, 594-603

Abstract: 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.

Keywords: Intelligent Traffic Management System; Artificial Intelligence; Adaptive Traffic Signal Control; Emergency Vehicle Prioritization; Computer Vision; Smart Transportation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i3:id:1643

DOI: 10.32628/IJSRST26133182

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