Versatile, but not focused, traffic offenders are more likely to be at fault for a fatal crash. (June 2022)

• Record Type: Journal Article
• Authors: Davey, Benjamin; Parkes, Alexander; Freeman, James; Mills, Laura; Davey, Jeremy
• Is Part Of: Journal of safety research. Volume 81(2022)
• Page Start: 143
• Page End: 152
• Publication Date: 2022-06
• Subjects: Versatile -- Traffic offence -- Crash risk -- Fatal crash -- Dangerous driving; Industrial safety -- Periodicals; Accidents -- Prevention -- Periodicals; Safety -- Periodicals; Accidents, Occupational -- Periodicals; Sécurité du travail -- Périodiques; Accidents -- Prévention -- Périodiques; Accidents -- Prevention; Industrial safety; Periodicals; 363.1
• DOI: 10.1016/j.jsr.2022.02.006
• Languages: English
• ISSNs: 0022-4375

Abstract

Highlights: Traffic offenses can predict a person's likelihood of being at fault for a crash. Versatile offenders pose the highest risk to road safety. People who drive while unlicensed are a particular group of concern. People who commit serious, criminal-type, traffic offenses are at risk. Abstract: Introduction: The aim of this study was to determine whether drivers who had received more traffic infringements were more likely to be at fault for the crash in which they were killed. Method: The current dataset was derived from the crash and traffic history records provided by the Queensland Department of Transport and Main Roads and Coroner's Court for every driver, with available records, who was killed in a crash in Queensland, Australia, between 2011 and 2019 ( N = 1, 136). The most common traffic offenses in the current sample were speeding, disobeying road rules, driving under the influence of drugs and alcohol, and unlicensed driving. Logistic regression models were used to compute odds ratios for the number of overall offenses, the number of specific offense types, and for specific offending profiles that were derived from the literature. Age, gender, and crash type were each controlled for by entering them into the initial blocks of the regression models. Results: After accounting for the variance associated with age, gender, and crash type, only the overall number of offenses and the number of unlicensed driving offenses predicted a significant change in a drivers' likelihood of being at fault for the crash that killed them. Furthermore, drivers who were identified as having versatile (i.e., multiple offenses from different categories) or criminal-type offense profiles (i.e., offenses that were considered to approximate criminal offenses) were each significantly more likely to be at fault for a fatal crash. Practical Applications: This study provided an important contribution by demonstrating how a more nuanced approach to understanding how a driver's traffic history might be used to identify drivers who are more at risk of being involved in a crash (i.e., for which they were at fault). The implications of these findings are discussed with recommendations and consideration for future research.