Dynamic Traffic Signal Optimization Congestion Management and Emergency Vehicle Prioritization

These delays have been linked to the traditional traffic signal systems that are used as they are time bound with pre-set schedules that do not take into account changing operational surroundings. The real life integration of technology into traffic management has the potential of alleviating these issues. In this paper, a Smart Traffic Management System that uses YOLOv8 object detection, IoT inter-signal communication and Real Time adaptive signal control is examined. The proposed system does automatic adjustments to signal times according to the density of the vehicles and gives higher priority to emergency vehicles to promote seamless traffic. Through the examination of existing systems, an analysis is conducted pointing out the issues revolving around those systems which this research aims to solve. The STMS incorporates including but not limited to USB cameras, Raspberry Pi, and sophisticated machine learning models to do real life identification and decision making. In consideration of modern day urban traffic troubles, it can be said with confidence that this system is able to terminate congestion with the help of emergency vehicles and also provide limitlessly scalable solutions. Future considerations will include identifying field implementation limitations and addressing them during the research.