Evaluating changes in driver behaviour: A risk profiling approach

Ellison, Adrian B.; Bliemer, Michiel C.J.; Greaves, Stephen

doi:10.1016/j.aap.2014.12.018

Cited by 45 publications

(11 citation statements)

References 30 publications

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“…Analyses of driver behaviour are frequent in transportation research. Some authors, including Ellison et al (2015), Underwood (2013), Jun et al (2011), Elias et al (2010 and Ayuso et al (2010), have shown that factors such as night driving, urban driving, speeding and highway driving are correlated with the risk of being involved in an accident and with the corresponding severity of that accident.…”

Section: Introductionmentioning

confidence: 99%

Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data

2018

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Citation: Ayuso, M., Guillén, M. and Nielsen, J. P. (2018). Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data. Transportation, This is the accepted version of the paper.This version of the publication may differ from the final published version.Permanent repository link: http://openaccess.city.ac.uk/19160/ Link to published version: http://dx. AbstractWe show how data collected from a GPS device can be incorporated in motor insurance ratemaking. The calculation of premium rates based upon driver behaviour represents an opportunity for the insurance sector. Our approach is based on count data regression models for frequency, where exposure is driven by the distance travelled and additional parameters that capture characteristics of automobile usage and which may affect claiming behaviour. We propose implementing a classical frequency model that is updated with telemetrics information. We illustrate the method using real data from usage-based insurance policies. Results show that not only the distance travelled by the driver, but also driver habits, significantly influence the expected number of accidents and, hence, the cost of insurance coverage. This paper provides a methodology including a transition pricing transferring knowledge and experience that the company already had before the telematics data arrived to the new world including telematics information.

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Section: Introductionmentioning

confidence: 99%

Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data

2018

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“…Various studies have explored the potential of telematics when applied to risks of road accidents, beginning in 1968 with a preliminary analysis by Vickrey (1968). More recently, several papers have examined the impact of new technologies on road safety and how driving habits can be measured (Shafique & Hato, 2015, Xu et al, 2015, Ellison, Bliemer, & Greaves, 2015, Ayuso, Guillen, & Pérez-Marín, 2014, Underwood, 2013, Jun, Guensler, & Ogle, 2011, Elias, Toledo, & Shiftan, 2010and Ayuso, Guillen and Alcañiz, 2010, while others have focused specifically on mileage and new risk factors that might be included in the ratemaking process, see Ayuso, Guillen, and Nielsen (2018) for an extended review. Recently, it has been proven that including standard telematics variables significantly improves risk assessment of insureds, therefore insurers should be able to tailor their products to the customers' risk profile (Baecke & Bocca, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Use of Telematics Devices to Improve Automobile Insurance Rates

et al. 2018

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Most automobile insurance databases contain a large number of policyholders with zero claims. This high frequency of zeros may reflect the fact that some insureds make little use of their vehicle, or that they do not wish to make a claim for small accidents in order to avoid an increase in their premium, but it might also be because of good driving. We analyse information on exposure to risk and driving habits using telematics data from a Pay-as-you-Drive sample of insureds. We include distance travelled per year as part of an offset in a zero-inflated Poisson model to predict the excess of zeros. We show the existence of a learning effect for large values of distance travelled, so that longer driving should result in higher premium, but there should be a discount for drivers that accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety.

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“…The most widely applicable use of DBPs is likely the ability to empirically test the effectiveness of changes to infrastructure, education campaigns or legislation on road safety outcomes (Ellison et al, 2015). This includes cost-benefit analyses by placing a value on the risk score.…”

Section: Discussionmentioning

confidence: 99%