Development of an Intelligent Transportation System for Next-Gen AI

Air pollution, longer travel times, and higher petroleum use are all outcomes of urban traffic congestion, which is an ongoing problem. This inquiry is primarily concerned with this matter. For efficient traffic management, up-to-the-minute information on the quantity and trajectory of vehicles is crucial. To address these issues, we offer a new approach that uses the YOLOv8 object identification paradigm. Our technology can quantify traffic patterns by using real-time monitoring and exact vehicle identification. Using this data, lawmakers and traffic engineers can make better choices to lessen gridlock, enhance air quality, and cut down on fuel usage.