Literature DB >> 11829296

Previous convictions or accidents and the risk of subsequent accidents of older drivers.

Geneviève Daigneault1, Pierre Joly, Jean-Yves Frigon.   

Abstract

The over-involvement of elderly drivers in collisions has a potentially adverse effect on highway safety. The question for most experts in traffic research is whether we can predict the individual risk of accidents and which variables are the best predictors, especially for this population. For a better understanding of the elderly drivers' problems, this study aimed to describe the most common types of accidents in the elderly population of drivers living in Quebec (> or = 65 years of age). The second objective of the study was to analyse the relationship between previous accidents or convictions and the risk of subsequent accidents. The results show that: (1) elderly drivers are characterised by error accidents involving more than one car, especially at intersections, (2) prior accidents are a better predictor for accident risk than prior convictions and (3) these trends steadily increase with each age group (drivers 65 years old to 80 years or more). The results are discussed in relation to the literature on risk behaviour of the elderly drivers.

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Mesh:

Year:  2002        PMID: 11829296     DOI: 10.1016/s0001-4575(01)00014-8

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


  3 in total

Review 1.  Practice parameter update: evaluation and management of driving risk in dementia: report of the Quality Standards Subcommittee of the American Academy of Neurology.

Authors:  D J Iverson; G S Gronseth; M A Reger; S Classen; R M Dubinsky; M Rizzo
Journal:  Neurology       Date:  2010-04-12       Impact factor: 11.800

2.  Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.

Authors:  Chen Wang; Lin Liu; Chengcheng Xu; Weitao Lv
Journal:  Int J Environ Res Public Health       Date:  2019-01-25       Impact factor: 3.390

3.  Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data.

Authors:  Peter Barraclough; Anders Af Wåhlberg; James Freeman; Barry Watson; Angela Watson
Journal:  PLoS One       Date:  2016-04-29       Impact factor: 3.240

  3 in total

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