EMS call volume predictions: A comparative study

Authors:
Hubert Setzler;Cem Saydam;Sungjune Park
Affiliations:
School of Business, Francis Marion University, P.O. Box 100547, Florence, SC 29501, USA;Business Information Systems and Operations Management Department, The Belk College of Business, The University of North Carolina at Charlotte, Charlotte, NC 28223-0001, USA;Business Information Systems and Operations Management Department, The Belk College of Business, The University of North Carolina at Charlotte, Charlotte, NC 28223-0001, USA
Venue:
Computers and Operations Research
Year:
2009

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Abstract

The demand for ambulances fluctuates throughout the week, depending on the day of week and, even more so, the time of day. Many emergency medical services (EMS) managers adjust the number of ambulances deployed using various demand pattern analyses, including moving averages. Simply forecasting the number of expected calls for an entire region does not allow managers to deploy their often-limited resources effectively so that emergency response time is minimized. In order for deployment plans, or even sophisticated optimization models, to be more effective, emergency call forecasts must be accurate for both time and location. For purposes of this study, we consider forecasts accurate for a 4x4sq.mile region if they are within +/-0.25 of actual calls for hourly forecasts and within +/-0.5 of actual calls for 3-h forecasts. An artificial neural network (ANN) designed to forecast demand volume of specific areas during different times of the day is compared to current industry practice for accuracy of prediction. Our study shows that both methods produce accurate forecasts for certain levels of time and space granularity. Results also suggest that the high level of space and time details in forecasts desired by EMS managers may be difficult to obtain regardless of which method is used.