Mobility Pattern

Human mobility patterns, the regularities in how people move across space and time, are a focus of intense research driven by the availability of large-scale mobility datasets. Current studies employ machine learning, particularly deep learning models like transformers and generative approaches, to analyze and predict these patterns, often incorporating spatio-temporal data and activity-based modeling to improve accuracy and realism. This research is crucial for diverse applications, including urban planning, public health (e.g., epidemic modeling), and transportation management, by providing insights into population movement and enabling more effective resource allocation and policy design. Improved understanding of mobility patterns through advanced modeling techniques is leading to more accurate predictions and better-informed decision-making across various sectors.