Predicting the safety performance associated with highway design decisions: A case study of the Sea to Sky Highway
Bibliographic record
Abstract
An important component of any highway design project is the explicit evaluation of safety, leading to the quantification of the safety impacts resulting from changes in highway design parameters and the safety associated with the level of design consistency. Quantification of these safety impacts supports the design process by allowing decision makers the opportunity to analyse the safety benefits in relation to the cost of the highway improvement. This trade-off analysis allows for the justification of highway infrastructure investment. The ability to accurately quantify safety impacts is achieved by using evaluation tools such as collision prediction models, collision modification factors, and measures of design consistency. Although described and available in the safety engineering literature for years, these tools are now becoming widely accepted, since their use responds to the need to quantify safety. This is in sharp contrast to many traditional highway safety assessments, which often relied solely on expert opinion and often failed to adequately support difficult design decisions. Realizing the value of quantifying the safety impacts associated with design decisions, the British Columbia Ministry of Transportation tested an explicit safety evaluation approach for a new design of a section of the scenic Sea to Sky Highway, located between Vancouver and Whistler, in southern British Columbia, Canada. This paper describes the study, illustrating the benefits of this approach for the design process. The paper also offers some improvement over existing safety evaluation techniques.Key words: safety evaluation, collision prediction, collision modification factors, highway design, safety evaluation techniques.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".