Road safety for fleets of vehicles
Bibliographic record
Abstract
Road safety for fleets of vehicles has been neglected in the insurance literature, mainly because appropriate data and methodology were not available. This article makes a threefold contribution: 1) Produce statistics on current fleets’ road safety offences and accidents using a panel of 20 years of data on truck fleets; 2) relate fleets’ offences to accidents; and 3) identify and classify the riskiest fleets for insurance ratemaking based on past experience in managing road safety. Our main technical innovation to the insurance literature is in the estimation of fleets’ distributions of accidents. For each fleet size (or group of sizes), we estimate the parameters of the negative binomial (NB) distribution of the annual number of accidents according to the characteristics of the fleets, the years, and the number of driver (DRV) and carrier (CAR) road safety violations accumulated in the previous year. When the NB model does not accurately predict the mathematical expectation of the number of accidents of larger fleets, we proceed in two steps. First, we estimate the probability of having zero accidents in a year, and then estimate the negative binomial distribution using the estimated probability of having zero accidents, to weight the zeros of each fleet. To achieve our third objective, we construct risk classes for the vehicle fleets using the predicted accident probabilities obtained from the estimated models. Our results show a substantial heterogeneity between fleets in terms of road safety. This information should be very useful for optimal insurance pricing and better incentives for road safety.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".