A systems-based framework to measure, predict, and manage fatigue

Abstract

Transportation operator fatigue is a pervasive cross-modal risk, still largely unmitigated, costly, and underreported. Effectively predicting fatigue risk can help stem this trend and improve transportation safety, performance, and well-being. Not surprisingly, there is growing interest in applying fatigue models to support and evaluate safety-critical initiatives, such as scheduling, accident investigation, and hours-of-service rule making and compliance. Yet there is limited empirical data about whether current fatigue models are used as intended, how effectively they predict fatigue, and how they might be improved. Though current models typically focus on two or three biological processes that impact fatigue, the literature presents many internal and external variables that influence fatigue, and current models largely neglect this more comprehensive suite of contributing factors needed to assess individual employee risk across variable work environments. The U.S. Department of Transportation Safety Council has recognized the need to assess the strengths and weaknesses of existing biomathematical fatigue models and to explore how to maximize efficacy, reliability, and validity. In this chapter, we review a sample of current fatigue models, identify performance gaps, and describe an overarching framework to guide next-generation fatigue model development. The proposed eight-state model offers a novel systems-based approach to integrate contributing internal and external fatigue factors and better measure, predict, and manage fatigue risk across transportation and other safety-critical operations. Keywords: Driver distraction; Language: en