Predicting mobile users' next location using geo-embedding model and multilayer attention mechanism
Title: Predicting mobile users' next location using the semantically enriched geo-embedding model and the multilayer attention mechanism
Title: Predicting mobile users' next location using the semantically enriched geo-embedding model and the multilayer attention mechanism
Title: “Green transit-oriented development”: Exploring the association between TOD and visible green space provision using street view dataAbstractEnv
Associations between streetscape characteristics at Chinese adolescents’ activity places and active travel patterns on weekdays and weekends
Trackintel: An open-source Python library for human mobility analysis
Title: CarbonVCA: A cadastral parcel-scale carbon emission forecasting framework for peak carbon emissions
Title: Gauging urban resilience in the United States during the COVID-19 pandemic via social network analysis
Title: Conserved quantities in human mobility: From locations to tripsAbstractQuantifying intra-person variability in travel choices is essential for
Title:Fast optimization for large scale logistics in complex urban systems using the hybrid sparrow search algorithmAbstractUrban logistics is vital t
Title:Estimating China’s poverty reduction efficiency by integrating multi-source geospatial data and deep learning techniquesAbstractPoverty threaten
Title:Extracting human perceptions from street view images for better assessing urban renewal potentialHighlightsHuman visual and emotional perception