Assessing Risk of Collision with Fleet Telematics for Usage-Based Insurance: Case Study from South Korean Car Rental Operations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".