标题： 预测救护车需求：一种时空核方法 显示英文标题

标题： Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach

摘要： 准确预测细粒度时间和地点的救护车需求对于救护车车队管理和动态部署至关重要。在这种情况下，大规模数据集通常表现出复杂的时空动态和高分辨率下的稀疏性。 我们提出了一种使用时空核密度估计（stKDE）的预测方法来应对这些挑战，并为加拿大多伦多的救护车需求提供空间密度预测，该需求随每小时间隔变化。 具体而言，我们将每个历史观测的空间核按其对当前预测任务的信息量进行加权。 我们构建了时空权重函数，以纳入救护车需求中的各种时序和空间模式，包括位置特定的季节性和短期序列依赖性。 这使我们能够提取出最有帮助的历史数据，并利用数据中的时空模式进行快速且准确的预测。 我们进一步提供了高效的估计和可定制的预测程序。 stKDE 对急救医疗服务行业的非专业人员来说易于使用和解释。与目前行业实践相比，它具有显著更高的统计准确性，且计算成本相当。 显示英文摘要

摘要： Predicting ambulance demand accurately at fine time and location scales is critical for ambulance fleet management and dynamic deployment. Large-scale datasets in this setting typically exhibit complex spatio-temporal dynamics and sparsity at high resolutions. We propose a predictive method using spatio-temporal kernel density estimation (stKDE) to address these challenges, and provide spatial density predictions for ambulance demand in Toronto, Canada as it varies over hourly intervals. Specifically, we weight the spatial kernel of each historical observation by its informativeness to the current predictive task. We construct spatio-temporal weight functions to incorporate various temporal and spatial patterns in ambulance demand, including location-specific seasonalities and short-term serial dependence. This allows us to draw out the most helpful historical data, and exploit spatio-temporal patterns in the data for accurate and fast predictions. We further provide efficient estimation and customizable prediction procedures. stKDE is easy to use and interpret by non-specialized personnel from the emergency medical service industry. It also has significantly higher statistical accuracy than the current industry practice, with a comparable amount of computational expense.