Traffic Management System

Traffic management systems aim to optimize traffic flow and enhance safety, primarily focusing on mitigating congestion and improving efficiency. Current research emphasizes data-driven approaches, employing deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as LSTMs), and transformers, often combined with techniques like Bayesian optimization and federated learning, to analyze diverse data sources including video, sensor data, and vehicular networks. These advancements are significant for improving urban planning, reducing environmental impact through optimized routing and reduced idling, and enhancing road safety through improved incident detection and response.