Predicting intentions to comply with speed limits using a ‘decision tree’ applied to an extended version of the theory of planned behaviour

Abstract

Speed is a major cause of road traffic accidents and deaths. Public authorities address this issue by reducing speed limits, for example by extending the 30 km/h speed limit throughout the urban area. This research is linked to the traffic-calming project in Angers (France). It is based on the Theory of Planned Behaviour (TPB) and the prediction of young drivers’ intentions to comply with speed limits. We tested a modified version of the TPB that includes variables related to beliefs and other variables taken from the Self-Report Habit Index (SRHI). Participants (n = 391, Mean Age = 22.4, SD = 3.8) completed a questionnaire including measures of the TPB components related to intentions to comply with the 30 km/h speed limit. Bayesian analysis confirmed the relevance of this model, which explained 53% of the variance of behavioural intention. By projecting these results on a decision tree, we were able to identify the most influential variables for predicting intentions. The interest of this decision tree is that it makes it possible to compare self-reported intentions and expected outcomes. The study provides support for politicians, researchers and communications officers who are responsible for implementing speed limit measures.