Assessing Risk of Collision with Fleet Telematics for Usage-Based Insurance: Case Study from South Korean Car Rental Operations

Fleet telematics is facilitating the adoption of new billing models in the automotive insurance industry. Advancements in the Internet of Things domain enable real-time transmission of data that can be utilized to monitor the location and status of vehicles and extract information on drivers’ behavior. While these developments can inform risk assessments of drivers of both private and corporate vehicles, existing research has mainly focused on usage-based insurance models for drivers of private vehicles. This paper analyzes naturalistic data from a sample of drivers ( N = 3,854) of corporate vehicles rented from a fleet rental company in South Korea in the year 2018. We compared different algorithms to classify collision-free and collision-involved drivers and found random forest to exhibit the highest area under the receiver operating characteristic (ROC) and precision-recall curves. Further investigation showed that both business- and driver-related variables were related to collision involvement. The amount of running driving time (i.e., non-idling time), trip frequency (i.e., per 1,000 km), and rapid speed changes were the most influential variables on collision involvement. Business-related variables (i.e., running driving time and trip frequency) can inform fleet rental companies on how to instate insurance and rental rates for their corporate customers; variables that indicate risky driver behaviors (i.e., rapid speed changes) can inform the design of feedback systems incorporated to telematics devices aimed at correcting risky behaviors. Our findings extend the knowledge on collision factors and provide insights into building models for commercial fleet services that are based on business and driver-related variables.