In-car usage-based insurance feedback strategies. A comparative driving simulator study

Ergonomics. 2016 Sep;59(9):1158-70. doi: 10.1080/00140139.2015.1127428. Epub 2016 Apr 25.

Abstract

Usage-Based Insurances (UBI) enable policyholders to actively reduce the impact of vehicle insurance costs by adopting a safer and more eco-friendly driving style. UBI is especially relevant for younger drivers, who are a high-risk population. The effectiveness of UBI should be enhanced by providing in-car feedback optimised for individual drivers. Thirty young novice drivers were therefore invited to complete six experimental drives with an in-car interface that provided real-time information on rewards gained, their driving behaviour and the speed limit. Reward size was either displayed directly in euro, indirectly as a relatively large amount of credits, or as a percentage of the maximum available bonus. Also, interfaces were investigated that provided partial information to reduce the potential for driver distraction. Compared to a control no-UBI condition, behaviour improved similarly across interfaces, suggesting that interface personalisation after an initial familiarisation period could be feasible without compromising feedback effectiveness. Practitioner Summary: User experiences and effects on driving behaviour of six in-car interfaces were compared. The interface provided information on driving behaviour and rewards in a UBI setting. Results suggest that some personalisation of interfaces may be an option after an initial familiarisation period as driving behaviour improved similarly across interfaces.

Keywords: Intelligent speed advisor; Pay-As-You-Drive; behavioural feedback; driver distraction; intelligent transport system.

Publication types

  • Comparative Study

MeSH terms

  • Accidents, Traffic* / prevention & control
  • Accidents, Traffic* / psychology
  • Age Factors
  • Automobile Driving / psychology*
  • Computer Simulation
  • Feedback
  • Female
  • Humans
  • Insurance, Accident
  • Male
  • Risk Reduction Behavior*
  • Safety*
  • Simulation Training / methods
  • Token Economy*
  • Young Adult