collaborators

6 papers

cs.AI2026

GPS-Enhanced Tourist Mobility Modeling with Seasonal Spatial Priors and LLM-Based Activity Chain Generation

Yifan Liu, Yanling Sang, Xishun Liao +6

Tourist mobility poses a distinct challenge for urban transportation planning. Unlike resident commuting, tourist travel is largely non-routine, attraction driven, and highly sensi…

cs.AI2026

Uncertainty-Aware Trip Purpose Inference from GPS Trajectories via POI Semantic Zones and Pareto Calibration

Bo Yang, Haoxuan Ma, Yifan Liu +4

Large-scale GPS trajectory data offer rich observations of human mobility, yet assigning trip purposes to detected stops remains challenging due to the absence of individual-level…

cs.LG2025

Beyond 9-to-5: A Generative Model for Augmenting Mobility Data of Underrepresented Shift Workers

Haoxuan Ma, Xishun Liao, Yifan Liu +2

This paper addresses a critical gap in urban mobility modeling by focusing on shift workers, a population segment comprising 15-20% of the workforce in industrialized societies yet…

cs.LG2025

Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion

Haoxuan Ma, Xishun Liao, Yifan Liu +4

Human mobility modeling is critical for urban planning and transportation management, yet existing approaches often lack the integration capabilities needed to handle diverse data…

cs.LG2025

Next-Generation Travel Demand Modeling with a Generative Framework for Household Activity Coordination

Xishun Liao, Haoxuan Ma, Yifan Liu +4

Travel demand models are critical tools for planning, policy, and mobility system design. Traditional activity-based models (ABMs), although grounded in behavioral theories, often…

cs.AI2025

Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMs

Yifan Liu, Xishun Liao, Haoxuan Ma +3

Understanding human mobility patterns has long been a challenging task in transportation modeling. Due to the difficulties in obtaining high-quality training datasets across divers…