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

• URL: http://arxiv.org/abs/2306.15927v2
• Date: Fri, 29 Sep 2023 02:02:28 GMT
• Title: Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
• Authors: Arash Hajisafi, Haowen Lin, Sina Shaham, Haoji Hu, Maria Despoina Siampou, Yao-Yi Chiang, Cyrus Shahabi
• Abstract summary: Busyness Graph Neural Network (BysGNN) is a temporal graph neural network designed to learn and uncover the underlying multi-context correlations. By incorporating all contextual, temporal, and spatial signals, we observe a significant improvement in our forecasting accuracy over state-of-the-art forecasting models.
• Score: 9.670949636600035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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