Evaluating Safety Benefits of the Insurance Corporation of British Columbia Road Improvement Program Using a Full Bayes Approach
Bibliographic record
Abstract
The objective of this study was to conduct a time-series (before-to-after) evaluation of the safety performance of a sample of locations that have been improved under the Insurance Corporation of British Columbia (ICBC) Road Improvement Program. The program started in 1989 when ICBC established partnerships with local road authorities in British Columbia, Canada, and works cooperatively to make sound investments in road safety improvements. The overall effectiveness of the road improvement program was assessed by determining whether the frequency or severity of collisions at the improvement sites has been reduced after implementation of the improvement, and by quantifying the program costs versus the economic safety benefits to determine the return on ICBC’s road safety investment. Seventy-two urban intersections were included in the evaluation. The methodology adopted for estimating the safety benefits was a before–after study with the full Bayesian method, while the benefit–cost analysis was carried out by using two indicators: net present value and benefit–cost ratio (B/C) with a payback period of 5 years. Overall, the total reductions of severe (fatal plus injury) and nonsevere (property damage only) collision frequency for intersections with new pedestrian signal installations were found equal to −24.54% and −6.21%, respectively; −22.95% and −10.78%, respectively, for intersections with geometric design improvements; and −13.76% and −5.04%, respectively, for intersections with traffic signal upgrades. Finally, an overall B/C ratio of 4.32:1 was achieved.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".