6 papers
MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning
Fanjin Meng, Yuan Yuan, Jingtao Ding +3
Mobility Foundation Models (MFMs) have advanced the modeling of human movement patterns, yet they face a ceiling due to limitations in data scale and semantic understanding. While…
Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning
Yuan Yuan, Yukun Liu, Chonghua Han +2
Human mobility is a fundamental pillar of urban science and sustainability, providing critical insights into energy consumption, carbon emissions, and public health. However, the d…
MoveGPT: Scaling Mobility Foundation Models with Spatially-Aware Mixture of Experts
Chonghua Han, Yuan Yuan, Jingtao Ding +3
The success of foundation models in language has inspired a new wave of general-purpose models for human mobility. However, existing approaches struggle to scale effectively due to…
UniMove: A Unified Model for Multi-city Human Mobility Prediction
Chonghua Han, Yuan Yuan, Yukun Liu +3
Human mobility prediction is vital for urban planning, transportation optimization, and personalized services. However, the inherent randomness, non-uniform time intervals, and com…
Diffusion Transformers as Open-World Spatiotemporal Foundation Models
Yuan Yuan, Chonghua Han, Jingtao Ding +3
The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions. Effectively modeling these dynamics is essential…
UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction
Yuan Yuan, Jingtao Ding, Chonghua Han +3
Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Tradi…