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June 2011 Forecasting emergency medical service call arrival rates
David S. Matteson, Mathew W. McLean, Dawn B. Woodard, Shane G. Henderson
Ann. Appl. Stat. 5(2B): 1379-1406 (June 2011). DOI: 10.1214/10-AOAS442

Abstract

We introduce a new method for forecasting emergency call arrival rates that combines integer-valued time series models with a dynamic latent factor structure. Covariate information is captured via simple constraints on the factor loadings. We directly model the count-valued arrivals per hour, rather than using an artificial assumption of normality. This is crucial for the emergency medical service context, in which the volume of calls may be very low. Smoothing splines are used in estimating the factor levels and loadings to improve long-term forecasts. We impose time series structure at the hourly level, rather than at the daily level, capturing the fine-scale dependence in addition to the long-term structure.

Our analysis considers all emergency priority calls received by Toronto EMS between January 2007 and December 2008 for which an ambulance was dispatched. Empirical results demonstrate significantly reduced error in forecasting call arrival volume. To quantify the impact of reduced forecast errors, we design a queueing model simulation that approximates the dynamics of an ambulance system. The results show better performance as the forecasting method improves. This notion of quantifying the operational impact of improved statistical procedures may be of independent interest.

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David S. Matteson. Mathew W. McLean. Dawn B. Woodard. Shane G. Henderson. "Forecasting emergency medical service call arrival rates." Ann. Appl. Stat. 5 (2B) 1379 - 1406, June 2011. https://doi.org/10.1214/10-AOAS442

Information

Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1223.62161
MathSciNet: MR2849778
Digital Object Identifier: 10.1214/10-AOAS442

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 2B • June 2011
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