Nudging Drivers to Safety: Evidence from a Field Experiment

Driving is an integral component of many operational systems and any small improvement in driving quality can have a significant effect on accidents, traffic, pollution, and the economy in general. However, making improvements is challenging given the complexity and multidimensionality of driving as a task. We use telematics technology (i.e., real-time sensor data in a mobile device such as accelerometer and gyroscope) to measure driving performance as well as to deliver nudges to the drivers via notifications. Leveraging a smartphone application launched by our industry partners, we sent three types of performance nudges to drivers, indicating how they performed on the current trip with respect to their personal best, personal average, and latest driving performance. We find that personal best and personal average nudges improve driving performance, on average, by 18.17% and 18.71% standard deviations of the performance scores calculated by the application. This improvement translates into an increase in the inter-accident time by nearly 1.8 years, while also improving driving performance consistency as measured by the coefficient of variation of the performance score. Using generalized random forests we show that high-performing drivers who are not frequent feedback seekers benefit the most from personal best nudges, while low-performing drivers who are also frequent feedback seekers benefit the most from the personal average nudges. Using these findings, we construct personalized nudges that outperform both of these nudges.