Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health

Hal S. Stern; Daniel Blower; Michael L. Cohen; Charles A. Czeisler; David F. Dinges; Joel B. Greenhouse; Feng Guo; Richard J. Hanowski; Natalie P. Hartenbaum; Gerald P. Krueger; Melissa M. Mallis; Richard F. Pain; Matthew Rizzo; Esha Sinha; Dylan S. Small; Elizabeth A. Stuart; David H. Wegman

doi:10.1016/j.aap.2018.02.021

Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health

Hal S. Stern, Daniel Blower, Michael L. Cohen, Charles A. Czeisler, David F. Dinges, Joel B. Greenhouse, Feng Guo, Richard J. Hanowski, Natalie P. Hartenbaum, Gerald P. Krueger, Melissa M. Mallis, Richard F. Pain, Matthew Rizzo, Esha Sinha, Dylan S. Small, Elizabeth A. Stuart, David H. Wegman

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.

Original language	English (US)
Pages (from-to)	37-42
Number of pages	6
Journal	Accident Analysis and Prevention
Volume	126
DOIs	https://doi.org/10.1016/j.aap.2018.02.021
State	Published - May 2019

Keywords

Causal inference
Driver performance
Longitudinal studies
Observational studies
Obstructive sleep apnea

ASJC Scopus subject areas

Public Health, Environmental and Occupational Health
Safety, Risk, Reliability and Quality
Law
Human Factors and Ergonomics

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10.1016/j.aap.2018.02.021

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Stern, H. S., Blower, D., Cohen, M. L., Czeisler, C. A., Dinges, D. F., Greenhouse, J. B., Guo, F., Hanowski, R. J., Hartenbaum, N. P., Krueger, G. P., Mallis, M. M., Pain, R. F., Rizzo, M., Sinha, E., Small, D. S., Stuart, E. A., & Wegman, D. H. (2019). Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health. Accident Analysis and Prevention, 126, 37-42. https://doi.org/10.1016/j.aap.2018.02.021

Stern, HS, Blower, D, Cohen, ML, Czeisler, CA, Dinges, DF, Greenhouse, JB, Guo, F, Hanowski, RJ, Hartenbaum, NP, Krueger, GP, Mallis, MM, Pain, RF, Rizzo, M, Sinha, E, Small, DS, Stuart, EA & Wegman, DH 2019, 'Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health', Accident Analysis and Prevention, vol. 126, pp. 37-42. https://doi.org/10.1016/j.aap.2018.02.021

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abstract = "This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.",

keywords = "Causal inference, Driver performance, Longitudinal studies, Observational studies, Obstructive sleep apnea",

author = "Stern, {Hal S.} and Daniel Blower and Cohen, {Michael L.} and Czeisler, {Charles A.} and Dinges, {David F.} and Greenhouse, {Joel B.} and Feng Guo and Hanowski, {Richard J.} and Hartenbaum, {Natalie P.} and Krueger, {Gerald P.} and Mallis, {Melissa M.} and Pain, {Richard F.} and Matthew Rizzo and Esha Sinha and Small, {Dylan S.} and Stuart, {Elizabeth A.} and Wegman, {David H.}",

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year = "2019",

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doi = "10.1016/j.aap.2018.02.021",

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AU - Dinges, David F.

AU - Greenhouse, Joel B.

AU - Guo, Feng

AU - Hanowski, Richard J.

AU - Hartenbaum, Natalie P.

AU - Krueger, Gerald P.

AU - Mallis, Melissa M.

AU - Pain, Richard F.

AU - Rizzo, Matthew

AU - Sinha, Esha

AU - Small, Dylan S.

AU - Stuart, Elizabeth A.

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AB - This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.

KW - Causal inference

KW - Driver performance

KW - Longitudinal studies

KW - Observational studies

KW - Obstructive sleep apnea

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Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health

Abstract

Keywords

ASJC Scopus subject areas

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