Abstract: Urban traffic management is getting more and more difficult as the city expands and the number of vehicles increases. To address this, we put forward an AI-Based Dynamic Traffic Management System with Real-Time Detection & Priority Signal Optimization that, through computer vision and object identification, can effectively monitor and direct traffic flow. Conventional systems usually rely on pre-programmed timers or physical sensors, which can lead to bad timing of signals and slow reaction to real road conditions. Our system eliminates the use of these external sensors since it uses live video feeds to identify vehicles and pedestrians in real-time. It constantly monitors traffic density and flow patterns, enabling traffic signals to adjust dynamically instead of adhering to a fixed schedule. One of the key advantages of this system is its capacity to give priority to emergency vehicles and increase pedestrian safety at crossings. This makes emergency responses quicker and walkways safer for the public. By shifting from static, sensor-based techniques and embracing an AI-driven, vision-based solution, the system provides a more intelligent, scalable, and affordable solution for traffic management in today's world.
Keywords: Dynamic Traffic Management, Real-Time Object Detection, AI-Based Traffic Control, Traffic Signal Optimization, Emergency Vehicle Prioritization, Pedestrian Safety, Computer Vision in Traffic Systems, Smart City Traffic Solutions, Adaptive Traffic Signal Timing
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DOI:
10.17148/IJARCCE.2025.14504
