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The scope and nature of the drowsy driving problem in New York state

Open access
McCartt, Anne T., Ribner, Stephen A., Pack, Allan I., Hammer, Mark C.·1996·Accident Analysis & Prevention

Abstract

A telephone survey was conducted of a random sample of New York State licensed drivers to determine the prevalence and circumstances of drowsy driving. Based on the survey responses, 54.6% of the drivers had driven while drowsy within the past year; 22.6% had ever fallen asleep at the wheel without having a crash, 2.8% had ever crashed when they fell asleep, and 1.9% had crashed when driving while drowsy. Of the reported crashes due to driving while drowsy or falling asleep at the wheel, 82.5% involved the driver alone in the vehicle, 60.0% occurred between 11:00 p.m. and 7:00 a.m. 47.5% were drive-off-road crashes, and 40.0% occurred on a highway or expressway. Multiple regression analysis suggested that the following driver variables are predictive of an increased frequency of driving drowsy: demographic characteristics (younger drivers, more education, and men); sleep patterns (fewer hours of sleep at night and greater frequency of trouble staying awake during the day); work patterns (greater frequency of driving for job and working rotating shifts); and driving patterns (greater number of miles driven annually and fewer number of hours a person can drive before becoming drowsy).

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Metadata

Authors
McCartt, A. T., Ribner, S. A., Pack, A. I., & Hammer, M. C.
Venue
Accident Analysis & Prevention
Publisher
Elsevier BV
Published
1996-07
Volume
28
Issue
4
Pages
511-517
ISSN
0001-4575 (ISSN-L)
OpenAlex
W2106877513
Cited by
196
References
0
FWCI
6.46
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: The scope and nature of the drowsy driving problem in New York state. https://app.cochat.ai/papers/10.1016/0001-4575(96)00021-8
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McCartt, A. T., Ribner, S. A., Pack, A. I., & Hammer, M. C. (1996). The scope and nature of the drowsy driving problem in New York state. Accident Analysis & Prevention. https://doi.org/10.1016/0001-4575(96)00021-8

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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