Modeling severity of single vehicle run-off-road crashes in rural areas: model comparison and selection
Bibliographic record
Abstract
Run-off-road (ROR) crashes account for a large proportion of fatalities and serious injuries to vehicle occupants, especially in rural areas. While performing crash severity analysis using discrete choice models (DCMs), researchers may be confused by the following questions: first, should an ordered or unordered model structure be used and secondly, which modeling level is more appropriate, basic or advanced? A model selection framework is developed considering the following factors: (1) model structure — ordered or unordered; (2) intrinsic deficiency of each model; (3) computational burdens; (4) complexity of parameter interpretation; and (5) model fitness. Historical ROR crash data were utilized to illustrate how to choose an appropriate DCM based on the proposed framework. Using statistical tests and comparison of evaluation and validation measurements, both the mixed logit model and the partial proportional odds model yield a reasonable performance. All factors that significantly affect the severity level of a single-vehicle ROR crash were identified as well.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".