The study presents the Busyness Graph Neural Network (BysGNN), designed to forecast visits to urban Points-of-Interest (POIs). BysGNN uncovers multi-context correlations among POIs across temporal, spatial, and semantic dimensions, resulting in a comprehensive dynamic graph. This approach significantly improves forecasting accuracy over traditional methods, offering a deeper understanding of urban dynamics.
Arash Hajisafi,
Haowen Lin,
Sina Shaham,
Haoji Hu,
Maria Despoina Siampou,
Yao-Yi Chiang,
Cyrus Shahabi
