Hum Factors. 2014 Mar;56(2):287-305. doi: 10.1177/0018720813491283.
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.
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.
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.
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.
The model performed well under both routine and nonroutine conditions and over different patterns of workload variation.
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.
本研究旨在开发一种模型，以预测常规和非常规条件下一线操作人员所经历的心理工作量的变化。
工作量过大是安全关键型行业需要管理的风险。需要预测模型来有效管理这种风险，但开发预测模型却很困难。造成这种困难的很大一部分原因是工作量预测是一个多层次的问题。
在研究 I 中，使用从空中交通管制中心收集的数据开发了一个多层次的工作量模型。动态密度指标用于预测工作单元内和工作单元之间的工作量变化，同时控制评分者之间的变化。该模型在研究 2 和 3 中使用高保真模拟器进行了交叉验证。
在研究 2 和 3 中，报告的工作量通常保持在 90%预测区间的范围内。仅在非常规条件下，工作量才会越过预测区间的上限。定性分析表明，非例行事件导致工作量越过预测区间的上限，因为控制器无法战略性地管理其工作量。
该模型在常规和非常规条件下以及不同的工作量变化模式下表现良好。
工作量预测模型可用于支持战略和战术性的工作量管理。战略用途包括对历史和预计工作流程的分析以及对人员配备需求的评估。战术用途包括动态重新分配资源以满足需求的变化。
