High Order Mobility Feature

High-order mobility features encompass the complex patterns and regularities in movement data, aiming to improve the understanding and prediction of human and robotic mobility. Current research focuses on developing advanced models, such as generative pre-trained transformers (GPT) and Koopman models, to capture these intricate patterns from various data sources (e.g., GPS traces, sensor data) and across different spatial, temporal, and semantic dimensions. This work is significant for applications ranging from personalized services and improved urban planning to enhanced navigation systems for autonomous vehicles and robots operating in challenging environments. The development of robust and efficient algorithms for extracting and utilizing these features is crucial for advancing these diverse applications.