Traffic Congestion Estimation using YOLO Transfer Learning

Traffic congestion is a persistent problem in metropolises, leading to delays, excessive fuel consumption, and environmental pollution. Conventional traffic monitoring systems employ embedded sensors and manual supervision that do not provide real-time and scalable solutions. This paper offers a deep learning-based congestion prediction system employing YOLO transfer learning to detect vehicles, analyze traffic density, and classify congestion levels dynamically. The model is trained on real-world datasets, making it robust under varying lighting, weather, and traffic conditions. Unlike conventional approaches, this system monitors real-time traffic, allowing authorities to make data-driven judgments. An adaptive traffic signal management method is implemented to optimize signal durations based on congestion severity, enhancing traffic flow efficiency and minimizing wait times at crossings. By merging computer vision and artificial intelligence, this technique optimizes urban traffic management, contributes to smart city projects, and lays the groundwork for future developments in intelligent transportation systems. Future developments include refining the model for edge computing, including predictive traffic analytics, and scaling the system for large-scale implementation in urban cities.