Urban Mobility

Urban mobility research focuses on optimizing the movement of people and goods within cities, aiming to improve efficiency, sustainability, and accessibility. Current research employs machine learning, particularly deep learning models like transformers and graph neural networks, along with statistical methods, to predict travel patterns, manage disruptions in public transit, and optimize resource allocation (e.g., taxi dispatching, parking space management). These advancements offer significant potential for enhancing the reliability and responsiveness of urban transportation systems, leading to improved quality of life and reduced environmental impact. Furthermore, the use of large language models is emerging as a tool for synthesizing mobility data and generating realistic simulation scenarios.