Urban Flow

Urban flow research focuses on modeling and predicting the movement of people, vehicles, and other entities within cities, aiming to optimize urban systems and improve resource allocation. Current research employs diverse machine learning approaches, including deep reinforcement learning (for optimizing autonomous navigation and traffic signal control), physics-guided machine learning (for enhancing the accuracy and interpretability of flow predictions), and generative models (for creating realistic simulations of urban flows under various conditions, including those with limited data). These advancements have significant implications for urban planning, transportation management, and the development of efficient and equitable urban infrastructure.