Naturalistic field study of the restart break in US commercial motor vehicle drivers: Truck driving, sleep, and fatigue

Sparrow, Amy R.; Mollicone, Daniel J.; Kan, Kevin; Bartels, Rachel L.; Satterfield, Brieann C.; Riedy, Samantha M.; Unice, Aaron; Dongen, Hans P A Van

doi:10.1016/j.aap.2016.04.019

Cited by 40 publications

(34 citation statements)

References 26 publications

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“…There are two approaches used in the literature to study the drowsy driving behavior among long-haul truck drivers: (1) Naturalistic driving studies and (2) Questionnaire-based surveys. Naturalistic driving studies have been adopted by very few researchers in the past (Chen et al, 2016;Sparrow et al, 2016;Blanco et al, 2011). Whereas, questionnaire-based surveys or analyzing past crash records have been popularly used by the researchers, because these are safer data collection methods and cover a larger population sample (Sullman et al, 2002;Pérez-Chada et al, 2005;McCartt et al, 2000;Kanazawa et al, 2006).…”

Section: Previous Researchmentioning

confidence: 99%

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Mahajan

Velaga

Kumar

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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The aim of the study is to identify and model the role of payment incentives, driver work-rest patterns and other lifestyle habits influencing the drowsy driving behavior among long-haul truck drivers. To achieve this aim, this study targeted two main objectives: (1) to examine the significant differences between the groups of drowsy and non-drowsy drivers based on the opportunities of monetary incentives and (2) to examine the role of different factors: driver demographics, work-rest patterns, lifestyle and occupational characteristics particularly incentives associated with driving in causing driver sleepiness among Indian truck drivers. The study is based on interview responses from 453 long-haul truck drivers approached in three Indian cities-Mumbai, Indore and Nagpur. Initial principal component analysis of the responses related to financial incentives (occupational characteristics) resulted into two correlated factors: (i) willingness to earn extra payments if offered (WEP) and (ii) incentives available in the current driving experience (ICD) that influence driver work-rest patterns and alertness while driving. Kruskal-Wallis test showed a significant difference among the groups of sleepy and non-sleepy drivers due to these factors (WEP and ICD). Finally, a logistic regression model showed that long driving duration, working days per week, rest patterns, insufficient sleeping hours and history of violations were found significantly associated with drowsy driving among the long-haul truck drivers. Increase in consumption of caffeine and tobacco indicated reduction in driver alertness. According to the model results, the odds of drowsy driving were 77% less for drivers between 46-55 years compared to the young drivers (<25 years). Driving under the influence of financial incentives was observed to increase the odds of falling asleep by 1.58 times among the truck drivers. This was apparently the most interesting and intriguing result of the study indicating the need for further research on the influence of financial or socioeconomic motivations to sleepiness.

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Section: Previous Researchmentioning

confidence: 99%

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Mahajan

Velaga

Kumar

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

View full text Add to dashboard Cite

show abstract

“…Sleep estimates for EMS must take on-duty sleep into account, especially considering the prevalence of 24-hour duty periods. Data from actigraphy-and/or sleep diary-based, naturalistic studies can inform the development of customized probabilistic distribution of sleep times across EMS duty period types (47), which would improve model based analysis of available schedules.…”

Section: Prospective Use Of Biomathematical Models Of Fatigue In Emsmentioning

confidence: 99%

Does Implementation of Biomathematical Models Mitigate Fatigue and Fatigue-related Risks in Emergency Medical Services Operations? A Systematic Review

James¹,

Waggoner²,

Weiss³

et al. 2018

Prehospital Emergency Care

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This systematic review identified no studies that investigated the impact of biomathematical models in EMS operations. Findings from one study of non-EMS shift workers were favorable toward use of biomathematical models as a fatigue mitigation scheduling aid, albeit with very low quality of evidence pertaining to EMS operations. We propose three focus areas of research priorities that, if addressed, could help better elucidate the utility and impact of biomathematical models as a fatigue-mitigation tool in the EMS environment.

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“…Male drivers with less professional driving experience and higher body mass index were also associated with increased driving risk. Sparrow et al [18] used data from smart wristbands, psychomotor vigilance tests and subjective sleepiness rated through the Karolinska Sleepiness Scale (KSS) to assess the effects of sleep patterns on the driving performance of 106 professional drivers. Using a mixed-effects ANOVA approach, the authors concluded that non-working periods covering at least two nights help to mitigate drivers' fatigue, providing a greater opportunity for sleep recuperation.…”

Section: Related Workmentioning

confidence: 99%

Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

Lobo

Ferreira

Couto

2020

Sensors

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Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.

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Naturalistic field study of the restart break in US commercial motor vehicle drivers: Truck driving, sleep, and fatigue

Cited by 40 publications

References 26 publications

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Does Implementation of Biomathematical Models Mitigate Fatigue and Fatigue-related Risks in Emergency Medical Services Operations? A Systematic Review

Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

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