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.

翻译：准确预测城市区域兴趣点（POI）的访问量对于城市规划、交通管理、公共卫生及社会研究等多个应用领域的规划与决策至关重要。尽管该预测问题可被建模为多元时间序列预测任务，但现有方法无法充分挖掘POI间动态变化的多上下文关联。为此，我们提出繁忙度图神经网络（BysGNN），这是一种时序图神经网络，旨在学习并揭示POI间潜在的多上下文关联以实现精准访问量预测。不同于仅利用时间序列数据学习动态图的其他方法，BysGNN综合利用所有上下文信息及时间序列数据来学习精确的动态图表示。在基于美国真实数据集的实验中，通过融合所有上下文、时空信号，我们观察到所提模型的预测精度显著超越当前最先进的预测模型。