Combined Ranking Method for Screening Collision Monitoring Locations Along Alberta Highways
Bibliographic record
Abstract
This paper examines the results of combining two common screening methods: Critical Collision Rate and Weighted Severity, to develop an effective and practical method for identifying intersection sites for further on-site evaluation. The critical collision rate screening method has been used widely among practitioners to adjust for high collision rates resulting from low traffic volumes; however, the critical collision rate method does not account for collision severity. Conversely, the collision severity method equates the severity of collision to a common base, but does not account for traffic exposure and the rate in which collisions are occurring. Alberta Transportation developed a collision screening method that combines a weighted severity with the critical rate of collisions. This combined method is simple, efficient and more accurate than basic screening methods and accounts for collision severity as well as the traffic exposure. The analysis shows combining these two common screening methods provides the greatest number of special monitoring locations (locations with three or more similar collisions in five years), multiple severe collision types, highest traffic exposure and greatest number of collisions occurring at at-grade intersections when compared to other screening methods. (A) For the covering abstract of this conference see record control number 201111RT334E.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".