Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
This addresses urban planning and management needs by improving POI visit forecasting, but it is incremental as it builds on existing graph neural network methods.
The paper tackles the problem of forecasting visits to Points-of-Interest (POIs) by proposing BysGNN, a temporal graph neural network that learns dynamic graphs from all contextual information, resulting in significant accuracy improvements over state-of-the-art models in experiments with real-world datasets.
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.