Traffic accidents involving fatigue driving and their extent of casualties

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

260 Scopus Citations
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Original languageEnglish
Pages (from-to)34-42
Journal / PublicationAccident Analysis and Prevention
Volume87
Online published27 Nov 2015
Publication statusPublished - Feb 2016

Abstract

The rapid progress of motorization has increased the number of traffic-related casualties. Although fatigue driving is a major cause of traffic accidents, the public remains not rather aware of its potential harmfulness. Fatigue driving has been termed as a "silent killer." Thus, a thorough study of traffic accidents and the risk factors associated with fatigue-related casualties is of utmost importance. In this study, we analyze traffic accident data for the period 2006-2010 in Guangdong Province, China. The study data were extracted from the traffic accident database of China's Public Security Department. A logistic regression model is used to assess the effect of driver characteristics, type of vehicles, road conditions, and environmental factors on fatigue-related traffic accident occurrence and severity. On the one hand, male drivers, trucks, driving during midnight to dawn, and morning rush hours are identified as risk factors of fatigue-related crashes but do not necessarily result in severe casualties. Driving at night without street-lights contributes to fatigue-related crashes and severe casualties. On the other hand, while factors such as less experienced drivers, unsafe vehicle status, slippery roads, driving at night with street-lights, and weekends do not have significant effect on fatigue-related crashes, yet accidents associated with these factors are likely to have severe casualties. The empirical results of the present study have important policy implications on the reduction of fatigue-related crashes as well as their severity.

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Citation Format(s)

Traffic accidents involving fatigue driving and their extent of casualties. / Zhang, Guangnan; Yau, Kelvin K.W.; Zhang, Xun et al.
In: Accident Analysis and Prevention, Vol. 87, 02.2016, p. 34-42.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review