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Driver characteristics : What have we learnt and what do we still need to know? Vingilis, E

By: Vingilis, EPublication details: Alcohol, drugs and traffic safety, 2000; T2000, Stockholm, May 22-26, 2000. Paper, Description: 15 sSubject(s): Sweden | Conference | Drunken driving | Drugs | Alcohol | Driver | Properties | Demography | Behaviour | Personality | Motivation | Cognition | Human factor | Motivation | 842Bibl.nr: VTI P4030:15Location: Abstract: This paper is about what we have learnt about drinking and drug- using drivers in the last 50 years, and what we need to learn if we are to make further advances in reducing impaired driving behaviour. Because of the incredible breadth of the topic, I will only be able to provide a cursory reflection of the literature and future directions. However, I hope that I can at least paint some generic and somewhat universal profiles of impaired drivers for your consideration. That said, it is important to realise that profiles are dependent on data source. For example, DWI data generally indicate a much lower prevalence of female impaired drivers than self-report data. In addition, each jurisdiction and period in time have their own unique patterns of driver characteristics. This paper, however, will attempt to present the patterns and profiles that seem common across jurisdictions and time periods. Driver characteristics can fall under four domains; 1) demographic; 2) lifestyle behaviours; 3) personality, motivation and emotions; and 4) cognition and information processing.
Item type: Reports, conferences, monographs
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This paper is about what we have learnt about drinking and drug- using drivers in the last 50 years, and what we need to learn if we are to make further advances in reducing impaired driving behaviour. Because of the incredible breadth of the topic, I will only be able to provide a cursory reflection of the literature and future directions. However, I hope that I can at least paint some generic and somewhat universal profiles of impaired drivers for your consideration. That said, it is important to realise that profiles are dependent on data source. For example, DWI data generally indicate a much lower prevalence of female impaired drivers than self-report data. In addition, each jurisdiction and period in time have their own unique patterns of driver characteristics. This paper, however, will attempt to present the patterns and profiles that seem common across jurisdictions and time periods. Driver characteristics can fall under four domains; 1) demographic; 2) lifestyle behaviours; 3) personality, motivation and emotions; and 4) cognition and information processing.

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