• Hritesh Rishi
• Ved Parkash
• Dr. Deepak Kumar

In recent years, the surge in urbanization and vehicular population has escalated traffic congestion, posing significant challenges to efficient traffic management and emergency response. This review paper explores the integration of deep learning techniques in traffic management systems, emphasizing real-time prioritization of emergency vehicles. We analyze various deep learning models and algorithms used for traffic prediction, congestion management, and dynamic signal control. Additionally, we examine how these models are trained using large-scale traffic data and their deployment in smart traffic infrastructure. Special focus is given to the development and implementation of real-time emergency vehicle priority systems, which are crucial for minimizing response times and enhancing public safety. The review discusses the current state-of-the-art technologies, their performance, scalability, and the potential improvements needed for future advancements. By integrating deep learning with traffic management, this paper highlights the transformative potential of intelligent systems in creating more responsive and adaptive urban traffic environments, ultimately aiming to improve overall traffic flow and emergency service efficiency.

Deep Learning Traffic Management Emergency Vehicle Priority Real-Time System.

"A REVIEW ON INTEGRATED DEEP LEARNING-BASED TRAFFIC MANAGEMENT SYSTEM WITH REAL-TIME EMERGENCY VEHICLE PRIORITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.b98-b105, July-2024, Available :http://www.jetir.org/papers/JETIR2407113.pdf

"A REVIEW ON INTEGRATED DEEP LEARNING-BASED TRAFFIC MANAGEMENT SYSTEM WITH REAL-TIME EMERGENCY VEHICLE PRIORITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppb98-b105, July-2024, Available at : http://www.jetir.org/papers/JETIR2407113.pdf