Quantitative Evaluation of Highway Safety Performance Based on Design Consistency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Highway designers can theoretically improve roadway safety by evaluating design consistency. Research has identified four major areas in which the most promising consistency measures fall: operating speed, vehicle stability, alignment indices, and driver workload. Previous research primarily has focused on developing models to estimate consistency measures, with secondary focus on quantitatively relating safety performance to these measures. The authors discuss a study to quantify relationships between individual and combined consistency measures to actual collision experience through regression analysis. A database of horizontal curves representing different classes of two-lane rural highways in Eastern Ontario provided a study model. Researchers developed several statistically significant consistency measures to collision frequency relationship models which allowed examination of safety performance sensitivity for each model. These models may be used in safety-focused highway design because they represent a quantitative evaluation tool for design improvement safety benefits.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it