Predicting performance and safety based on driver fatigue
Abstract
Fatigue causes decrements in vigilant attention and reaction time and is a major safety hazard in the trucking industry. There is a need to quantify the relationship between driver fatigue and safety in terms of operationally relevant measures. Hard-braking events are a suitable measure for this purpose as they are relatively easily observed and are correlated with collisions and near-crashes. We developed an analytic approach that predicts driver fatigue based on a biomathematical model and then estimates hard-braking events as a function of predicted fatigue, controlling for time of day to account for systematic variations in exposure (traffic density). The analysis used de-identified data from a previously published, naturalistic field study of 106 U.S. commercial motor vehicle (CMV) drivers. Data analyzed included drivers' official duty logs, sleep patterns measured around the clock using wrist actigraphy, and continuous recording of vehicle data to capture hard-braking events. The curve relating predicted fatigue to hard-braking events showed that the frequency of hard-braking events increased as predicted fatigue levels worsened. For each increment on the fatigue scale, the frequency of hard-braking events increased by 7.8%. The results provide proof of concept for a novel approach that predicts fatigue based on drivers' sleep patterns and estimates driving performance in terms of an operational metric related to safety. The approach can be translated to practice by CMV operators to achieve a fatigue risk profile specific to their own settings, in order to support data-driven decisions about fatigue countermeasures that cost-effectively deliver quantifiable operational benefits.
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The link between fatigue and risk of road traffic accidents is well established, including in car drivers and rail freight-regulating employees.
“The link between fatigue and risk of road traffic accidents is well established, including in car drivers [20] and rail freight-regulating employees [21].”
From Vitamins and Minerals for Energy, Fatigue and Cognition: A Narrative Review of the Biochemical and Clinical Evidence (2020) · cites this paper as [20]
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Tardy, A.-L., Pouteau, E., Marquez, D., Yilmaz, C., & Scholey, A. (2020) · Nutrients · doi:10.3390/nu12010228 · Cited by 541 · see citing works
- Authors
- Mollicone, D., Kan, K., Mott, C., Bartels, R., Bruneau, S., van Wollen, M., Sparrow, A. R., & Van Dongen, H. P. A.
- Affiliations
- Pulsar Informatics (United States) (US)
- Washington State University (US)
- Corresponding author
- Mollicone, Daniel
- Venue
- Accident Analysis & Prevention
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 126
- Pages
- 142-145
- ISSN
- 0001-4575 (ISSN-L)
- OpenAlex
- W2796017944
- Cited by
- 85
- References
- 19
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 18 · 2023: 9 · 2022: 7 · 2021: 18 · 2020: 13 · 2019: 6 · 2018: 1
- FWCI
- 12.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Accident Analysis & Prevention (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Federal Motor Carrier Safety Administration (DTMC7514D00011)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- driver fatigue, commercial drivers, fatigue risk management, sleep patterns, actigraphy, road safety, near-crashes, collision risk, fatigue countermeasures
- MeSH terms
- Accidents, Traffic, Adult, Attention, Automobile Driving, Fatigue, Female, Humans, Male, Middle Aged, Reaction Time, Sleep, Wakefulness, Risk Assessment, Motor Vehicles
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