6 papers
Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement
Maria Despoina Siampou, Shushman Choudhury, Shang-Ling Hsu +2
Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POI…
TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning
Zhen Xiong, Shang-Ling Hsu, Cyrus Shahabi
Learning generalizable trajectory representations from raw GPS traces remains difficult because the data is continuous, noisy, and irregularly sampled. Spatial tokenization is also…
TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond
Shang-Ling Hsu, Mark Tenzer, Cyrus Shahabi +1
Human mobility differs from text and from generic time series in three structural ways: visits are tuple-valued events whose meaning depends on the joint distribution over location…
POIFormer: A Transformer-Based Framework for Accurate and Scalable Point-of-Interest Attribution
Nripsuta Ani Saxena, Shang-Ling Hsu, Mehul Shetty +3
Accurately attributing user visits to specific Points of Interest (POIs) is a foundational task for mobility analytics, personalized services, marketing and urban planning. However…
Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors
Ziyao Li, Shang-Ling Hsu, Cyrus Shahabi
Understanding human mobility behavior is crucial for numerous applications, including crowd management, location-based recommendations, and the estimation of pandemic spread. Machi…
TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal Model
Shang-Ling Hsu, Emmanuel Tung, John Krumm +2
Human mobility modeling from GPS-trajectories and synthetic trajectory generation are crucial for various applications, such as urban planning, disaster management and epidemiology…