Speeding is a leading predictor of car crashes
August 23, 2019, Kitchener, Ontario
Posted by: Robert Deutschmann, Personal Injury Lawyer
The news has been filled the last day or so with a new study from the University of Waterloo. It analysed the computer data from 28 million cars and determined that the best predictor of crash (based on that data) is the speed of the driver. It is important to note that this data did not include any information on the behavior of the driver that was not recorded by the car’s computer. A key point here is that following too closely (tail gating) was not included and it may also be closely related to accident rates and speeding as they are both related to aggressive driving.
The data were collected from onboard computers installed by insurance companies in Ontario and Texas. The onboard trackers confirm what was already suspected by insurance companies but now is proven. Speed and accidents are related closely.
The study was limited to four predictive behaviors that the onboard computers could track – speeding, hard braking, hard acceleration and hard cornering. The conclusion is that the faster you are moving the higher the probability of getting into a crash. Driver age and model of car driven does not alter this relationship.
Changing the culture of speeding is not easy. As anyone who drives on Ontario highways knows, enforcement of speed limits is limited at best. Traffic seems to flow at a an average of 120 km/h in this area, with many cars moving far faster than that. The culture of speeding will only change when drivers fear the repercussions of being caught. Effective inducements can take the form of more officers catching drivers, photo radar (which is extremely effective at slowing down traffic), huge penalties from insurers using onboard devices to determine how much you speed, or autonomous cars that have speed limiters.
The report is available to read, and here is the 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.
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