Opportunity

In 2021, over 20 million drivers in the United States obtained usage-based insurance (UBI) policies.

UBI policies enable drivers to earn discounts on their premiums based on safe driving habits tracked through smartphone telematics apps. Smartphone telematics apps use data algorithms to collect driving data and measure risky behaviors like hand-held phone use, speeding, hard braking, and fast acceleration. This technology holds great promise for studying and improving driving behavior. However, more research is needed to determine optimal strategies for changing behavior on a meaningful scale.

Intervention 

We are working in partnership with the American Automobile Association Foundation for Traffic Safety (AAA FTS) to implement a nationwide randomized trial to determine whether evidence-based strategies for promoting safer driving can be optimized by tailoring the scope of feedback and the selection of goals for behavior change.

The study will compare the effect of focused vs. standard feedback and self-chosen vs. assigned behavior goals on overall crash risk. Participants will be randomly assigned to one of the four arms: 1) control (no feedback or incentive), 2) standard feedback with UBI-like incentive, 3) assigned focus area with UBI-like incentive, or 4) self-chosen focus area with UBI-like incentive.

We will use Way to Drive, our homegrown smartphone telematics app, for this study.  

Impact

As a first step, we validated the algorithm used to classify trips according to whether the smartphone user was the driver or a non-driver. This will help ensure we collect appropriate data during the randomized trial. In a field study, the algorithm classified trips with 97 percent accuracy.

Intervention results will be posted here after the trial concludes.