Predicting Ambulance Demand: Challenges and Methods
This addresses operational efficiency for emergency services by providing better demand forecasts, though it appears incremental as it builds on existing statistical methods.
The paper tackled the problem of predicting ambulance demand at fine spatio-temporal resolutions to improve staff and fleet management, achieving significantly more accurate predictions than current industry practice for Toronto and Melbourne.
Predicting ambulance demand accurately at a fine resolution in time and space (e.g., every hour and 1 km$^2$) is critical for staff / fleet management and dynamic deployment. There are several challenges: though the dataset is typically large-scale, demand per time period and locality is almost always zero. The demand arises from complex urban geography and exhibits complex spatio-temporal patterns, both of which need to captured and exploited. To address these challenges, we propose three methods based on Gaussian mixture models, kernel density estimation, and kernel warping. These methods provide spatio-temporal predictions for Toronto and Melbourne that are significantly more accurate than the current industry practice.