Investigation of model calibration issues in the safety performance assessment of Ontario highways
Bibliographic record
Abstract
A set of Safety Performance Function (SPFs) commonly known as accident prediction models, were developed for evaluating the safety of Highway segments under the jurisdiction of Ministry of Transportation, Ontario (MTO). A generalized linear modeling approach was used in which negative binomial regression models were delevoped separately for total accidents and for three severity types (Property Damage Only accidents, Fatal and Injury accidents) as a function of traffic volume AADT. The SPFs were calibrated from 100m homogenous segments as well as for variable length continuous segments that are homogeneous with respect to measured traffic and geometric characteristics. For the models calibrated for Rural 2-Lane Kings Highways, the variables that had significant effects on accident occurrence were the terrain, shoulder width and segment lenght. It was observed that the disperson parameter of the negative binomial districution is large for 100m segments and smaller for longer segments. Further investigation of the dispersion parameter for Rural 2-Lane Kings Highways showed that the models calibrated with a separate dispersion parameter for each site depending on the segment length performed better that the model calibrated considering fixed dispersion parameter for all sites. For Rural 2-Lane Kings Highways, a model was calibrated with trend considering each year as a separate observation. The GEE (Generalized Estimating Equation) procedure was use to develop these models since it incorporated the temporal correlation that exists in repeated measurements. Results showed that integration of time trend and temporal correlation in the model improves the model fit.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".