Effect of Winter Events on Highway Performance in the Province of Alberta
Bibliographic record
Abstract
A vital component of asset management, performance measurement is used in planning and programming to identify assets and/or processes that are over/under performing. As part of the move to asset management, Alberta Transportation has implemented performance based planning and monitoring of the provincial highway network and three performance measures, based upon technical measurements, are used. These measures relate to network condition, functional adequacy and utilization. Although Alberta, like the rest of Canada and much of North America, is a winter province, no clear suite of performance measure has been developed for monitoring the effectiveness of snow and ice control measures during winter weather events. Traditionally, agencies have measured inputs (such as salt or sand) or outputs (such as plowing frequencies), but none of the existing measures address effectiveness. Using data from weigh-in-motion (WIM) sensors and regional weather data from Environment Canada, the effect on mean vehicular speed of various winter events was determined at six sites across the provincial highway system. Reduction in vehicular speed and the duration of the speed reduction (time to recovery) were calculated for five event types and differences noted. The methodology developed shows promise for future development of robust, repeatable and easily understood performance measures that can be used to monitor winter events and to develop future benchmarks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".