TY - JOUR
T1 - The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta
AU - Channouf, Nabil
AU - L'Ecuyer, Pierre
AU - Ingolfsson, Armann
AU - Avramidis, Athanassios N.
N1 - Funding Information:
Acknowledgements This work has been supported by NSERC-Canada Grants no. ODGP-0110050 and CRDPJ-251320, a grant from Bell Canada via the Bell University Laboratories, and a Canada Research Chair to P. L’Ecuyer as well as NSERC-Canada Grant no. 203534 to A. Ingolfsson. We thank the Calgary EMS department (particularly Heather Klein-Swormink and Tom Sampson) for making this work possible, J. Cheng, E. Erkut, D. Haight, and T. Riehl for useful discussions and assistance with data preparation, and the anonymous referees for useful comments.
PY - 2007/2
Y1 - 2007/2
N2 - We develop and evaluate time-series models of call volume to the emergency medical service of a major Canadian city. Our objective is to offer simple and effective models that could be used for realistic simulation of the system and for forecasting daily and hourly call volumes. Notable features of the analyzed time series are: a positive trend, daily, weekly, and yearly seasonal cycles, special-day effects, and positive autocorrelation. We estimate models of daily volumes via two approaches: (1) autoregressive models of data obtained after eliminating trend, seasonality, and special-day effects; and (2) doubly-seasonal ARIMA models with special-day effects. We compare the estimated models in terms of goodness-of-fit and forecasting accuracy. We also consider two possibilities for the hourly model: (3) a multinomial distribution for the vector of number of calls in each hour conditional on the total volume of calls during the day and (4) fitting a time series to the data at the hourly level. For our data, (1) and (3) are superior.
AB - We develop and evaluate time-series models of call volume to the emergency medical service of a major Canadian city. Our objective is to offer simple and effective models that could be used for realistic simulation of the system and for forecasting daily and hourly call volumes. Notable features of the analyzed time series are: a positive trend, daily, weekly, and yearly seasonal cycles, special-day effects, and positive autocorrelation. We estimate models of daily volumes via two approaches: (1) autoregressive models of data obtained after eliminating trend, seasonality, and special-day effects; and (2) doubly-seasonal ARIMA models with special-day effects. We compare the estimated models in terms of goodness-of-fit and forecasting accuracy. We also consider two possibilities for the hourly model: (3) a multinomial distribution for the vector of number of calls in each hour conditional on the total volume of calls during the day and (4) fitting a time series to the data at the hourly level. For our data, (1) and (3) are superior.
KW - Arrivals
KW - Emergency medical service
KW - Forecasting
KW - Simulation
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=33846014236&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33846014236&partnerID=8YFLogxK
U2 - 10.1007/s10729-006-9006-3
DO - 10.1007/s10729-006-9006-3
M3 - Article
C2 - 17323653
AN - SCOPUS:33846014236
SN - 1386-9620
VL - 10
SP - 25
EP - 45
JO - Health Care Management Science
JF - Health Care Management Science
IS - 1
ER -