A. (Angèle) Picco, MSc

Road traffic accidents are a public health concern. Young drivers are at risk due to inexperience and overconfidence, whereas older drivers are at risk due to physical and cognitive decline. At the same time, mobility is an important correlate of quality-of-life. Vehicles are increasingly equipped with sensors, making it feasible to support the driver with feedback on driving performance. Large opportunities exist for data-driven feedback in facilitating behaviour change and a societal transition towards safer mobility. We envision feedback at various temporal levels, from short-term (real-time) to long-term (artificial intelligence accumulating knowledge about the driver’s style). The first aim is to examine how a data-driven approach can facilitate a transition towards safe mobility. Three use cases will be studied. First, we will examine how vehicle data can contribute towards valid assessment and feedback aid for young drivers, second, how personalised feedback can help older drivers remain mobile, and third, how monitoring and feedback can keep professional drivers safely on the road. The second aim is to examine whether a data-driven approach is scalable so that it evokes behaviour change at the population level. Evidently, driver data use raises important ethical questions, including matters of privacy, confidentiality, consent, security, and misuse, needed to be addressed if data are to be used on a population-wide scale. The project deliverables are (1) knowledge about driving skill and style assessment across temporal levels, (2) empirical evidence regarding behaviour change, (3) knowledge about how data can evoke a road safety transition at the societal level.