标题： 使用核扭曲预测墨尔本救护车需求 显示英文标题

标题： Predicting Melbourne Ambulance Demand using Kernel Warping

摘要： 准确预测救护车需求在空间和时间上的细粒度变化对于救护车车队管理和动态部署至关重要。典型的挑战包括高分辨率数据的稀疏性以及需要尊重复杂的城市空间区域。为了提供澳大利亚墨尔本救护车需求的空间密度预测（其随每小时间隔而变化），我们提出了一种预测时空核扭曲方法。为了预测每个小时的需求，我们在相关过去时间段（标记数据）中最相似的数据稀疏集合上构建了一个核密度估计器，但将这些核扭曲到更大的过去数据集合（点云）中，而不考虑时间段。点云代表了墨尔本的空间结构和地理特征，包括复杂的边界、道路网络和社区。借鉴流形学习的思想，核扭曲通过点云的图拉普拉斯算子完成，并可以解释为对空间特征施加的正则化和先验。核宽度和扭曲程度通过交叉验证高效估计，并且可以针对时间和/或位置进行特定设置。与当前行业实践、未扭曲的核密度估计和时变高斯混合模型相比，我们提出的模型给出了显著更准确的预测。 显示英文摘要

摘要： Predicting ambulance demand accurately in fine resolutions in space and time is critical for ambulance fleet management and dynamic deployment. Typical challenges include data sparsity at high resolutions and the need to respect complex urban spatial domains. To provide spatial density predictions for ambulance demand in Melbourne, Australia as it varies over hourly intervals, we propose a predictive spatio-temporal kernel warping method. To predict for each hour, we build a kernel density estimator on a sparse set of the most similar data from relevant past time periods (labeled data), but warp these kernels to a larger set of past data irregardless of time periods (point cloud). The point cloud represents the spatial structure and geographical characteristics of Melbourne, including complex boundaries, road networks, and neighborhoods. Borrowing from manifold learning, kernel warping is performed through a graph Laplacian of the point cloud and can be interpreted as a regularization towards, and a prior imposed, for spatial features. Kernel bandwidth and degree of warping are efficiently estimated via cross-validation, and can be made time- and/or location-specific. Our proposed model gives significantly more accurate predictions compared to a current industry practice, an unwarped kernel density estimation, and a time-varying Gaussian mixture model.