Investigating Effect of Collision Aggregation on Safety Evaluations with Models of Multivariate Linear Intervention
Bibliographic record
Abstract
This study investigated the effect of collision aggregation on safety evaluation through a case study from the 2001 Signal Head Upgrade Program of the Insurance Corporation of British Columbia, Canada. Three types of evaluations were performed. Bivariate intervention models were used in the first two evaluations to assess the impacts of different collision severity levels [severe and property damage only (PDO)] and the impact of the time of the collision (daytime and nighttime) on safety. In the third evaluation, multivariate intervention models were used to determine the safety impacts of the program on each combination of collision severity and time of occurrence (i.e., severe–daytime, severe–nighttime, PDO–daytime, PDO–nighttime). Overall, the results indicated that the program was effective in improving the safety of the treated intersections. However, the results revealed that aggregate analyses could lead to misleading results. Aggregation of collisions over time of day indicated that the treatment resulted in significant reductions in PDO collisions but not in severe collisions. Alternatively, aggregation of collisions over severity levels indicated that the treatment resulted in significant reductions in both daytime and nighttime collisions. These results were different from the results of the disaggregate analysis, in which significant reductions were found for all collision types, except for severe collisions during the daytime.
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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 | 0.085 | 0.203 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".