Literature DB >> 26625173

Traffic accidents involving fatigue driving and their extent of casualties.

Guangnan Zhang1, Kelvin K W Yau2, Xun Zhang3, Yanyan Li4.   

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.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Keywords:  Fatigue driving; Road safety; Traffic accident

Mesh:

Year:  2015        PMID: 26625173     DOI: 10.1016/j.aap.2015.10.033

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  17 in total

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3.  Driving Fatigue Detection from EEG Using a Modified PCANet Method.

Authors:  Yuliang Ma; Bin Chen; Rihui Li; Chushan Wang; Jun Wang; Qingshan She; Zhizeng Luo; Yingchun Zhang
Journal:  Comput Intell Neurosci       Date:  2019-07-14

4.  An Investigation into Unsafe Behaviors and Traffic Accidents Involving Unlicensed Drivers: A Perspective for Alignment Measurement.

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5.  Evaluating the Effects of Holidays on Road Crash Injuries in the United Kingdom.

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6.  Assessment of Combination of Automated Pupillometry and Heart Rate Variability to Detect Driving Fatigue.

Authors:  Lin Shi; Leilei Zheng; Danni Jin; Zheng Lin; Qiaoling Zhang; Mao Zhang
Journal:  Front Public Health       Date:  2022-02-21

7.  Spatiotemporal characteristics of elderly population's traffic accidents in Seoul using space-time cube and space-time kernel density estimation.

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Journal:  PLoS One       Date:  2018-05-16       Impact factor: 3.240

8.  Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018.

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Journal:  Mil Med Res       Date:  2020-03-10

9.  The impact of a meal, snack, or not eating during the night shift on simulated driving performance post-shift.

Authors:  Charlotte C Gupta; Stephanie Centofanti; Jillian Dorrian; Alison M Coates; Jacqueline M Stepien; David Kennaway; Gary Wittert; Leonie Heilbronn; Peter Catcheside; Georgia A Tuckwell; Daniel Coro; Dilushi Chandrakumar; Siobhan Banks
Journal:  Scand J Work Environ Health       Date:  2020-11-15       Impact factor: 5.024

10.  Sex Differences in the Amount and Patterns of Car-Driving Exposure in Spain, 2014 to 2017: An Application of a Quasi-Induced Exposure Approach.

Authors:  José Mateos-Granados; Luis Miguel Martín-delosReyes; Mario Rivera-Izquierdo; Eladio Jiménez-Mejías; Virginia Martínez-Ruiz; Pablo Lardelli-Claret
Journal:  Int J Environ Res Public Health       Date:  2021-12-16       Impact factor: 3.390

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