Understanding the predictors of young drivers' speeding intention and behaviour in a three-month longitudinal study. (March 2021)

• Record Type: Journal Article
• Authors: Vankov, Daniel; Schroeter, Ronald; Twisk, Divera
• Is Part Of: Accident analysis and prevention. Volume 151(2021)
• Publication Date: 2021-03
• Subjects: Road safety -- Speeding -- Young drivers; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105859
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Drivers sensitive to rewards are more likely to speed. Drivers with less driving experience or with higher behavioural control speed less. Drivers who sped more in the past are most likely to speed more in the future. Abstract: This study aimed to examine to what extent an Adolescent Speeding Specific Model (ASSM), extending the theory of planned behaviour (TPB), predicts young drivers' (aged 18–25) future and past speeding (n = 126). The ASSM tested the contribution of demographics, split TPB, additional predictors and past behaviour to young drivers' speeding at two moments of time, over three months. Hierarchical multiple regression revealed that participants most likely to speed in the future were those who have done so in the past (independent predictor (ip): past compliance with the speed limit), and who were not certain in their ability to control their speeding ( ip: self-efficacy ). ASSM also indicated that people who reported speeding at T1 did so at T2 as well (ip: past compliance with the speed limit ). The results also show that sensitive to rewards people would speed more (ip: sensitivity to reward), similar to ones with less control over their behaviour (ip: perceived controllability ) or with more driving experience (ip: GDL phase ). Overall, the ASSM explained 73% of the intention to comply with speed limits variation and 62% of the present compliance with the speed limit variation. Compared to models, similar in structure to ASSM, our model explained variance in intention, equal to the previously maximum observed, and 22% more variance in behaviour . As a result, our findings may help design better targeted educational campaigns to prevent young drivers' speeding.