A Personalised Intervention Model for Improving the Effectiveness of Driving-Behaviour Apps

Abstract

Driving behaviour is key to determining the safety of individuals on the road. It can be argued that understanding driving behaviour and developing methods to improve it will lead to a decrease in accidents and improve citizen safety. At present, most of the work associated with driving behaviour is carried out by insurance companies who use mobile apps and telematic sensors to monitor driving behaviours. These companies are, mainly, capturing driving data to calculate annual premiums rather than to share that data with the drivers. On the academic side, the work focuses on feedback approach and real-time warnings systems. Both commercial and academic research does not consider the significant fact that all drivers are not the same; one-size-fits-all" will not work. This research investigates the scope of personalisation by factors such as age, gender, culture, country and type of driving (e.g. rural or urban) and its impact on driver behaviour. The aim is to improve the effectiveness of driving behaviours systems which can produce meaningful feedback to the driver. Our model suggests that through personalisation, user-modelling and persuasive techniques such as regular feedback reports to drivers (showing their bad driving behaviour), it is possible to improve driving styles and eventually create improved driving behaviour systems. Another positive outcome of this model will be safer roads. We have conducted surveys, used focus groups and interviews to find out the types of driver and their preferences.