Traffic Flow Learning

Traffic flow learning aims to develop accurate and interpretable models of traffic dynamics, enabling better prediction, control, and optimization of transportation systems. Current research focuses on leveraging deep learning architectures, such as graph neural networks, recurrent neural networks (like LSTMs), and diffusion models, often combined with techniques like Koopman operator theory to enhance model interpretability and incorporate physical constraints. These advancements are improving traffic simulation, enabling proactive resource allocation (e.g., UAV trajectory planning), and facilitating the development of more robust autonomous driving systems by generating challenging, realistic driving scenarios.