  Forecasting the number of visits to Points-of-Interest (POI) in an urban area
is critical for planning and decision-making for various application domains,
from urban planning and transportation management to public health and social
studies. Although this forecasting problem can be formulated as a multivariate
time-series forecasting task, the current approaches cannot fully exploit the
ever-changing multi-context correlations among POIs. Therefore, we propose
Busyness Graph Neural Network (BysGNN), a temporal graph neural network
designed to learn and uncover the underlying multi-context correlations between
POIs for accurate visit forecasting. Unlike other approaches where only
time-series data is used to learn a dynamic graph, BysGNN utilizes all
contextual information and time-series data to learn an accurate dynamic graph
representation. By incorporating all contextual, temporal, and spatial signals,
we observe a significant improvement in our forecasting accuracy over
state-of-the-art forecasting models in our experiments with real-world datasets
across the United States.
