@article{ref1,
title="Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health",
journal="Accident analysis and prevention",
year="2019",
author="Stern, Hal S. and Blower, Daniel and Cohen, Michael L. and Czeisler, Charles A. and Dinges, David F. and Greenhouse, Joel B. and Guo, Feng and Hanowski, Richard J. and Hartenbaum, Natalie P. and Krueger, Gerald P. and Mallis, Melissa M. and Pain, Richard F. and Rizzo, Matthew and Sinha, Esha and Small, Dylan S. and Stuart, Elizabeth A. and Wegman, David H.",
volume="126",
number="",
pages="37-42",
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.<br><br>Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2018.02.021",
url="http://dx.doi.org/10.1016/j.aap.2018.02.021"
}