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dc.contributorVirginia Polytechnic Institute and State University. Transportation Instituteen
dc.contributorNational Surface Transportation Safety Center for Excellenceen
dc.contributor.authorSoccolich, Susan A.en
dc.contributor.authorHickman, Jeffrey S.en
dc.contributor.authorHanowski, Richard J.en
dc.date.accessioned2013-07-25T17:28:30Zen
dc.date.available2013-07-25T17:28:30Zen
dc.date.issued2011-06-30en
dc.identifier.other11-UF-012en
dc.identifier.urihttp://hdl.handle.net/10919/23321en
dc.description.abstractThe current report investigated the 'high-risk' driver concept, and predictors associated with group membership, in a sample of 200 CMV drivers using naturalistic data from the Drowsy Driver Warning System Field Operational Test and the Naturalistic Truck Driving Study. A cluster analysis revealed three distinct groups of drivers (safe, average, and risky) based on the rate of safety-critical events per mile traveled. The risky group accounted for 50.3% of the total safety-critical events, but only 7.1% of the total miles traveled. Various anthropometric and demographic variables were found to have an association to group membership; however, these relationships were weak (mainly due to the small sample size). The current study found support for the high-risk driver concept; future research should focus on identifying risky drivers so that targeted safety management techniques can be used to improve driving behavior. -- Report website.en
dc.description.statementofresponsibilitySusan Soccolich, Jeffrey Hickman and Richard Hanowskien
dc.format.extentix, 29 pagesen
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherVirginia Tech. Virginia Tech Transportation Instituteen
dc.rightsCreative Commons CC0 1.0 Universal Public Domain Dedicationen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subject.lccTL230.3 .S63 2011eben
dc.subject.lcshTruck drivers -- Rating ofen
dc.subject.lcshTruck driving -- Evaluationen
dc.subject.lcshTruck accidents -- Preventionen
dc.subject.lcshCommercial vehicle industry -- Accidents -- Preventionen
dc.titleIdentifying high-risk commercial truck drivers using a naturalistic approachen
dc.typeReporten
dc.identifier.urlhttp://scholar.lib.vt.edu/VTTI/reports/HighRiskCMVDriversFinalReport_06302011.pdfen
dc.identifier.oclc746318102en
dc.type.dcmitypeTexten


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Creative Commons CC0 1.0 Universal Public Domain Dedication
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