Predicting Melbourne Ambulance Demand using Kernel Warping

TL;DR

This paper introduces a novel kernel warping method for predicting ambulance demand in Melbourne, effectively capturing complex spatial features and improving accuracy over existing models by integrating manifold learning techniques.

Contribution

The paper proposes a new spatio-temporal kernel warping approach that incorporates urban spatial structures via graph Laplacian regularization, enhancing demand prediction accuracy.

Findings

Significantly improves prediction accuracy over industry practice.

Effectively captures complex urban spatial features.

Adapts kernel bandwidth and warping degree dynamically.

Abstract

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.