Effects of Vehicle Mass and Other Parameters on Driver Relative Fatality Risk in Vehicle-Vehicle Crashes
Bibliographic record
Abstract
Regression models are used to understand the relative fatality risk for drivers in front-front and front-left crashes. The field accident data used for the regressions were extracted by NHTSA from the FARS database for model years 2000-2007 vehicles in calendar years 2002-2008. Multiple logistic regressions are structured and carried out to model a log-linear relationship between risk ratio and the independent vehicle and driver parameters. For front-front crashes, the regression identifies mass ratio, belt use, and driver age as statistically significant parameters (p-values less than 1%) associated with the risk ratio. The vehicle type and presence of the ESC are found to be related with less statistical significance (p-values between 1% and 5%). For front-left crashes the driver risk ratio is also found to have a log-log linear relationship with vehicle mass ratio. The driver risk ratio relationship to the vehicle mass ratio is consistent with conservation of momentum and can be used with the literature reported relationship between absolute risk and velocity change, to predict the dependence of the driver fatality risk on its own mass when crashing into other vehicles in a fleet. For front-front crashes, the model indicates that when the subject vehicle mass is at half the fleet mean mass, the risk for the driver is about 3 times that of a driver in a vehicle at the fleet average mass, and it is about 50%, when the vehicle mass is twice the fleet average. The difficulty in defining independent effects from size and mass using existing field data is discussed for the front-front crash data. Results show that out of the vehicle mass ratio, wheelbase ratio, and footprint ratio, the mass ratio is the best single predictor of the risk ratio: combining these “size” ratios with the mass ratio degrades the fit due to the high correlations between them. Examination of conditional risk shows that, for front-front crashes, the driver fatality does not show a significant dependence on vehicle wheelbase when the mass ratio of the two vehicles is already taken into account.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".