Short interval forecasting of emergency phone call (911) work loads

This article explores the use of six different forecasting techniques for predicting daily emergency call workloads for the Indianapolis Police Department’s communications area. Historical call volume data are used to estimate the model parameters. A hold-out sample of five months compares forecasts and actual daily call levels. The forecast system utilizes a rolling horizon approach, where daily forecasts are made for the coming month from the end of the prior month. The forecase origin is then advanced to the end of the month, where the current month’s actual call data are added to the historical database new parameters are estimated, and then the next month’s daily estimates are generated. Error measures of residual standard deviations, mean absolute percentage error, and bias are used to measure performance. Statistical analyses are conducted to evaluate if significant differences in performance are present among the six models. The research presented in this article indicates that there are clearly significant differences in performance for the six models analyzed. These models were tailored to the specific structure and this work suggests that the short interval forecasting problems faced by many service organizations has several structural differences compared to the typical manufacturing firm in a made-to-stock environment. The results also suggests two other points. First, simple modeling approaches can perform well in complex environments that are present in many service organizations. Second special tailoring of the forecasting model is necessary for many service firms. Historical data patterns for these organizations tend to be more complex than just trend and seasonal elements, which are normally tracked in smoothing models. These are important conclusions for both managers of operating systems and staff analysts supporting these operating systems. The design of an appropriate forecasting system to support effective staff planning must consider the nature, scope, and complexity of these environments.