Performance Measures for Inter-Agency Comparison of Road Networks Safety
Bibliographic record
Abstract
A study carried out for the Transportation Association of Canada (TAC) in 2011 focussed on the key performance measures needed for effective management of rural road network infrastructure, with emphasis on system preservation and safety. The latter area, as described in this paper, noted that the current state-of-practice in Canada uses accident rate per million vehicle - km of travel (MVKT) as the most common measure. This is also the case for various other international jurisdictions. A framework for road network performance measures is defined in the paper. It includes safety as a key component and emphasizes that the measures should integrate the objectives involved with stakeholder interests and tie in to transportation values. Recommended performance measures for safety in the TAC Study are categorized into three tiers, with Tier 1 incorporating collision rate and fatality rate per MVKT. Comparison and communication of safety performance in the TAC Study is recommended to consist of a distribution plot of agency 3-year mean values; then the agency's overall average collision rate and fatality rate would be compared to the national average using standard deviations to determine whether the record is above or below the national average. Best practices for obtaining the necessary data underlying performance measures are also recommended in the paper. (A) For the covring abstract of this paper see ITRD record number 201211RT334E.
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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 | 0.094 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".