Published Paper Details:

The problem of managing traffic in contemporary cities has become more difficult due to the exponential increase in vehicle traffic and rapid urbanization. When it comes to managing dynamic and unpredictable road conditions, traditional methods--from manual police regulation to fixed-timer signal systems and limited sensor-based approaches--are becoming less and less effective. These inefficiencies exacerbate environmental issues by increasing fuel consumption and greenhouse gas emissions in addition to causing lengthy delays and driver stress. Recent studies have focused on intelligent traffic management systems that use computer vision, machine learning, and artificial intelligence (AI) to overcome these drawbacks. One of the most popular real-time object detection frameworks among them is You Only Look Once (YOLO), which provides excellent vehicle recognition accuracy and efficiency in a variety of traffic situations. With an emphasis on vehicle detection, density estimation, and adaptive signal control, this survey compiles and examines developments in YOLO-based traffic signal optimization. We examine previous works that incorporate YOLO into intelligent transportation systems, evaluate how well they perform in comparison to conventional and hybrid approaches, and emphasize how they can lower traffic, travel delays, and energy usage in general. The study also lists unresolved issues like robustness in inclement weather or low visibility, hardware constraints for real-time processing, and scalability to extensive road networks. This work offers an organized viewpoint on how YOLO-based systems can develop within larger smart city frameworks by incorporating insights from current research trends. The survey's ultimate goal is to act as a reference. point for further research, connecting computer vision methods powered by AI with practical intelligent traffic management applications.