3 papers
cs.AI2025
Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Xinglei Wang, Tao Cheng, Stephen Law +6
Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised…
cs.CE2025
Enriching Location Representation with Detailed Semantic Information
Junyuan Liu, Xinglei Wang, Tao Cheng
Spatial representations that capture both structural and semantic characteristics of urban environments are essential for urban modeling. Traditional spatial embeddings often prior…
cs.AI2024
Multimodal Contrastive Learning of Urban Space Representations from POI Data
Xinglei Wang, Tao Cheng, Stephen Law +3
Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spa…