Traffic accidents involving fatigue driving and their extent of casualties

Accid Anal Prev. 2016 Feb:87:34-42. doi: 10.1016/j.aap.2015.10.033. Epub 2015 Nov 27.

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.

Keywords: Fatigue driving; Road safety; Traffic accident.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Accidents, Traffic / mortality*
  • Adult
  • Aged
  • China / epidemiology
  • Cross-Sectional Studies
  • Darkness
  • Environment Design / statistics & numerical data*
  • Fatigue / complications*
  • Fatigue / mortality*
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Motor Vehicles / statistics & numerical data*
  • Risk Factors
  • Safety
  • Young Adult