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Category: Project

Description: The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviours are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, traffic safety work is unlikely to progress by attributing dangerous behaviour to stable properties of individuals, whereas investigating direct relationships between traffic behaviours offers a fruitful way forward. Network models of driver behaviour and background factors influencing behaviour were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviours is reported. Finally, a model predicting crashes based on prior behaviour was built and tested in separate datasets. This contribution helps to bridge a gap between experimental / theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviours, while network analysis offers a way to do so for self-report studies.