VTechWorks staff will be away for the winter holidays starting Tuesday, December 24, 2024, through Wednesday, January 1, 2025, and will not be replying to requests during this time. Thank you for your patience, and happy holidays!
 

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

Simple item page
dc.contributor.authorStern, Hal S.en
dc.contributor.authorBlower, Danielen
dc.contributor.authorCohen, Michael L.en
dc.contributor.authorCzeisler, Charles A.en
dc.contributor.authorDinges, David F.en
dc.contributor.authorGreenhouse, Joel B.en
dc.contributor.authorGuo, Fengen
dc.contributor.authorHanowski, Richard J.en
dc.contributor.authorHartenbaum, Natalie P.en
dc.contributor.authorKrueger, Gerald P.en
dc.contributor.authorMallis, Melissa M.en
dc.contributor.authorPain, Richard F.en
dc.contributor.authorRizzo, Matthewen
dc.contributor.authorSinha, Eshaen
dc.contributor.authorSmall, Dylan S.en
dc.contributor.authorStuart, Elizabeth A.en
dc.contributor.authorWegman, David H.en
dc.contributor.departmentStatisticsen
dc.contributor.departmentVirginia Tech Transportation Instituteen
dc.date.accessioned2019-08-26T14:19:51Zen
dc.date.available2019-08-26T14:19:51Zen
dc.date.issued2019-05en
dc.description.abstractThis 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.en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.aap.2018.02.021en
dc.identifier.eissn1879-2057en
dc.identifier.issn0001-4575en
dc.identifier.pmid29530304en
dc.identifier.urihttp://hdl.handle.net/10919/93261en
dc.identifier.volume126en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectCausal inferenceen
dc.subjectDriver performanceen
dc.subjectLongitudinal studiesen
dc.subjectObservational studiesen
dc.subjectObstructive sleep apneaen
dc.titleData and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver healthen
dc.title.serialAccident Analysis and Preventionen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1-s2.0-S0001457518300836-main.pdf
Size:
195.1 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Scholarly Works, Virginia Tech Transportation Institute
Scholarly Works, Statistics