Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting

TL;DR

This paper introduces BysGNN, a novel temporal graph neural network that leverages all available contextual information to dynamically model POI relationships, significantly improving visit forecasting accuracy.

Contribution

The paper presents BysGNN, a new method that fully exploits multi-context data to learn dynamic graphs for more accurate POI visit prediction.

Findings

BysGNN outperforms state-of-the-art models in real-world datasets.

Incorporating all contextual signals improves forecasting accuracy.

Dynamic graph learning enhances understanding of POI visit patterns.

Abstract

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.