Drive safe and save money
Owning a car can be really expensive. The combined cost of fuel, maintenance, vehicle taxes, and vehicle insurance is a substantial burden for many households. Vehicle insurance companies traditionally determine what insurance premium to charge by assigning both vehicle and driver to a risk category, based on the characteristics and behaviours of categories that you happen to fall into. Depending on the jurisdiction, the criteria may include the vehicle’s age and model, the driver’s age, marital status, gender, place of residence, number of moving violations, and self-reported annual mileage (see e.g. http://en.wikipedia.org/wiki/Vehicle_insurance). This seems to make perfect sense. People with certain characteristics, such as young male drivers, run a high average risk of getting involved in accidents, which makes it only fair to set a high insurance premium for them. Other groups in turn benefit financially from driving more safely.
“However, it is not really fair, is it?”
An inexperienced but conscientious driver may in fact behave very safely in traffic, while an individual in a safe driver category may have a personality disorder resulting in aggressive driving behaviour and a tendency to break the speed limit. The point is, risk categorisation can, and maybe should, be taken much further by taking into account the individual’s actual driving behaviour. In other words pay-as-you-drive; also known as pay-how-you-drive or usage-based insurance. In fact, pay-as-you-drive insurance policies have been available for over a decade, especially in Anglo-Saxon countries, but also in the Netherlands (http://dekilometerverzekering.nl; http://www.polisvoormij.nl; https://fairzekering.nl/).
“There are many types of pay-as-you-drive insurance.”
Ranging from pre-paid options, where customers pre-purchase a certain number of miles and are then charged based on additional mileage driven, to full behaviour based telematics pay-as-you-drive insurance, where drivers can be rewarded or penalised based on not only how much they drive, but also on where, when, and in particular how they drive (Litman, 2005, 2011; Bordoff and Noel, 2008). This financial incentive structure is set up to stimulate a safe driving style, resulting in less accidents and ultimately in a reduction of insurance claims. In this way, a vehicle insurer earns back the cashbacks or premium discounts it gives to its customers.
The effectiveness of pay-as-you-drive insurance may depend on which driving variables are linked to rewards or penalties. In a recent study, carried out at the University of Groningen, Dijksterhuis et al. (2015) investigated a pay-as-you-drive system that encouraged ‘smooth’ driving – driving without harshly braking, accelerating and steering – and observing the speed limit. In this driving simulator study, speeding incidents were reduced by about 94%, while the number of harsh braking, acceleration, and steering events decreased by 76%, 71% and 86% respectively.
“This is an encouraging result, confirming that a pay-as-you-drive car insurance is an effective tool for positively changing driving behaviour.”
Pay-as-you-drive systems are most likely to encourage safe driving if drivers are given regular feedback on their driving performance and the associated rewards and penalties. In terms of the feedback provided by pay-as-you-drive telematics systems, a popular option used by many insurance companies is to provide feedback via a dedicated website. However, web-based feedback suffers from two major problems. The first is that web-based feedback is delayed, in that it is not available until the driver takes the time to check the website. The second is that customers may very rarely, or even never, take the time to check in on web-based feedback (Bolderdijk et al., 2011).
“An alternative to web-based feedback is in-car feedback where feedback on the driver’s behaviour and information about the rewards and penalties they have received is delivered to them as they drive.”
The latter alternative was hypothesized to have a larger impact on driving behaviour (Dijksterhuis et al., 2015). To test this hypothesis, participants were invited to the driving simulator twice. In the one week between sessions the group that received web-based feedback on financial savings and the related driving behaviour were asked to view a website containing personalised driving feedback. The group that received in-car feedback drove with an Ipod that was located on the dashboard which provided similar information while driving. As it turned out, our results showed that under these feedback conditions, both feedback types were highly effective as compared to a naïve control group.
“Since in-car feedback potentially distracts drivers, this result has an important implication, namely that in-car feedback may not be necessary to optimize feedback for pay-as-you-drive insurance products, as long as driver’s actually view the feedback.”
How to meet this last requirement? That is definitely a good topic for future research.
This blog is based on:
Other references:
Bolderdijk, J., Knockaert, J., Steg, E.M., & Verhoef, E.T. (2011). Effects of pay-as-you-drive vehicle insurance on young drivers’ speed choice: results of a Dutch field experiment. Accid Anal Prev, 43(3), 1181–1186.
Bordoff, J., & Noel, P. (2008). Pay-as-you-drive auto insurance: A simple way to reduce driving-related harms and increase equity. Hamilton Project Discussion Paper.
Litman, T. (2005). Pay-as-you-drive pricing and insurance regulatory objectives. J Insur Regul, 23(3), 35.
Litman, T. (2011). Pay-as-you-drive pricing for insurance affordability. Victoria Transport Policy Institute.
NOTE: Image by 401(K), licenced under CC BY 2.0
Chris Dijksterhuis is a postdoctoral researcher and a teacher at the Department of Clinical and Developmental Neuropsychology. His general interest is making the interaction between humans, technology, and their environment smarter, more effective and more enjoyable. For this purpose, he has investigated ways of complementing driver support systems, such as a cruise control, with information of the driver’s actual workload levels. This was estimated processing brainwave activities in real time. Recently, he investigated how to optimise the delivery of rewards, penalties and information provision for pay-as-you-drive vehicle insurance systems. His fields of interest include human machine interaction, the car-driver system, traffic psychology, human factors, behavioural adaptations, adaptive (driver) support systems, (semi) autonomous driving, smart mobility, mobility and ageing, physiology, neuropsychology, brain-computer interface, pay-as-you-drive systems.
Selected publications:
Dijksterhuis, C., Lewis-Evans, B., Jelijs, B., de Waard, D., Brookhuis, K.A., & Tucha, O. (2015). The impact of immediate or delayed feedback on driving behaviour in a simulated Pay-As-You-Drive system. Accident Analysis & Prevention, 75, 93-104.
Dijksterhuis, C., de Waard, D., Brookhuis, K.A., Mulder B., & de Jong, R. (2013). Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns. Frontiers in Neuroscience, 7, 149.
Dijksterhuis, C, Stuiver, A, Mulder, L.J.M., Brookhuis, K.A., & De Waard, D. (2012). An adaptive driver support system: user experiences and driving performance in a simulator. Human Factors, 54, 772-785.
Dijksterhuis, C., Brookhuis, K.A., & De Waard, D. (2011). Effects of steering demand on lane keeping behaviour, self-reports, and physiology. A simulator study. Accident Analysis and Prevention, 43, 1074-1081.
