Using telematics data to find risky driver behaviour

• Published in: Accident analysis and prevention 2019-10, Vol.131, p.131-136
• Main Authors: Winlaw, Manda, Steiner, Stefan H., MacKay, R. Jock, Hilal, Allaa R.
• Format: Article
• Language: English
• Subjects: Case-control study; Crash risk; Driving behaviour; Logistic regression; Pay-how-you-drive
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2019.06.003

•We present analysis of over 28 million vehicle trips.•We introduce a novel case/control methodology for studying automotive telematics data and the associated risk of a driver's behaviour.•We find that speeding is the most important driver behaviour linking driver behaviour to crash risk. Usage-based insurance schemes provide new opportunities for insurers to accurately price and manage risk. These schemes have the potential to better identify risky drivers which not only allows insurance companies to better price their products but it allows drivers to modify their behaviour to make roads safer and driving more efficient. However, for Usage-based insurance products, we need to better understand how driver behaviours influence the risk of a crash or an insurance claim. In this article, we present our analysis of automotive telematics data from over 28 million trips. We use a case control methodology to study the relationship between crash drivers and crash-free drivers and introduce an innovative method for determining control (crash-free) drivers. We fit a logistic regression model to our data and found that speeding was the most important driver behaviour linking driver behaviour to crash risk.