Full Bayes Before-and-After Evaluation of Traffic Safety Improvements in City of Edmonton, Canada
Bibliographic record
Abstract
The objective of this study was to evaluate the safety performance of a sample of intersections that had been improved with the implementation of certain safety countermeasures targeting right-turn collisions in the city of Edmonton, Canada. A full Bayes approach was used to determine the effectiveness of the improvements by employing a before-and-after design with matched (yoked) comparison groups. Three linear intervention models were considered: a multivariate model that modeled treatment effects as a gradual change, a similar model with the addition of a jump treatment effect, and a univariate model that specifically analyzed right-turn collisions. The results indicated that the safety improvement program was effective; up to 40% of right-turn collisions were reduced. Despite the small sample size, these reductions were statistically significant. The results show the usefulness of the full Bayes technique in performing before-and-after evaluations of traffic treatment programs and in eliminating the need for a reference population and also in allowing for additional types of analysis, including multivariate analysis (modeling collisions of different types and severities at the same time), temporal effects (for both treatment and long-term trends), and greater freedom in the selection of error structure.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".