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Development and validation of a multilevel model for predicting workload under routine and nonroutine conditions in
Objective:
The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions.
Background:
Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem.
Method:
A multilevel workload model was developed in Study I with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters.The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator.
Results:
Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically.
Conclusion:
The model performed well under both routine and nonroutine conditions and over different patterns of workload variation.
Application:
Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs.Tactical uses include the dynamic reallocation of resources to meet changes in demand.
