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Accid Anal Prev. 2019 Oct;131:131-136. doi: 10.1016/j.aap.2019.06.003. Epub 2019 Jun 25.

Using telematics data to find risky driver behaviour.

Author information

1
Department of Statistics and Actuarial Science, University of Waterloo, Canada.
2
Shopify, Canada.

Abstract

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.

KEYWORDS:

Case-control study; Crash risk; Driving behaviour; Logistic regression; Pay-how-you-drive

PMID:
31252331
DOI:
10.1016/j.aap.2019.06.003

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