Speeding has been identified as the riskiest kind of aggressive driving as well as a strong predictor of crashes, according to new telematics-based research.

The research could revolutionise how insurance premiums are set

Carried out by researchers at Canada’s University of Waterloo, the project examined data from 28 million trips for possible links between four bad driving behaviours – speeding, hard braking, hard acceleration and hard cornering – and the likelihood of crashes.

Their analysis revealed speeding is a strong predictor of crashes, while statistically significant links for the other kinds of aggressive driving couldn’t be established.

As such, the work highlights how telematics data on speeding drivers could provide an accurate picture of driver risk and be used to help set fair premiums in the future.

“For insurance companies using this telematics data to assess who is a good risk and who isn’t, our suggestion based on the data is to look at speed, at people driving too fast,” said Stefan Steiner, a statistics professor in Waterloo’s Faculty of Mathematics.

Data for the study came from insurance companies in Ontario and Texas with clients who had on-board diagnostic devices installed in their vehicles.

Said to be the first study of its kind, the project saw researchers initially analyse the data to identify 28 crashes based on indicators such as rapid deceleration.

Each vehicle in those crashes was then matched with 20 control vehicles that had not been in crashes, but were similar in terms of other characteristics, including geographic location and driving distance.

When the crash cases were compared to the control cases using a sophisticated penalty system for the four kinds of bad driving, speeding emerged as the key difference between them.

“Some of the results are no surprise, but prior to this we had a whole industry based on intuition,” said Allaa (Ella) Hilal, an adjunct professor of electrical and computer engineering. “Now it is formulated – we know aggressive driving has an impact.”

Steiner cautioned that the study was limited by several unknowns, such as different drivers using the same vehicle, and said more research was needed to verify the results.

But he said that the use of telematics data in the long term could “revolutionise” the insurance industry by enabling fairer, personalised premiums based on actual driving behaviour, not age, gender or location.

Hilal believes the data could also make roads safer by giving drivers both tangible evidence and financial incentives to change.

“Having this information exposed and understood allows people to wrap their minds around their true risks and improve their driving behaviours,” she said. “We are super pumped about its potential.”

Manda Winlaw, a former mathematics post-doctoral fellow, and statistics professor Jock MacKay also collaborated on the study, Using telematics data to find risky driver behaviour, which appears in the journal ‘Accident Analysis and Prevention’.