Quantification of Road Safety Risk at Locations Without Collisions to Justify Road Safety Investments
Bibliographic record
Abstract
Since creating its Road Improvement Program (RIP), the Insurance Corporation of British Columbia (ICBC) has invested more than $110 million in road safety improvements in British Columbia, Canada. The RIP partners with provincial road authorities to identify problem locations and implement interventions to improve road safety. The level of ICBC investment in a safety project has been based on the potential for collision reduction associated with the proposed improvements. The goal has been to target collision-prone locations to reduce frequency and severity of collisions and, therefore, auto insurance claim costs to ICBC. Although the projects funded by the RIP were entirely necessary and effective, the reactive nature of the program did not allow for investments at locations that were deemed to be high risk but that did not have a significant history of collisions. In recognition that some attention should be given to these high-risk, low-crash locations, a new program was developed to provide funding to support road improvement projects that were not based on a history of collisions. The new program, referred to as the Proactive Road Safety Program, complemented the reactive program through the provision of funding support for projects that could prevent, rather than reduce, collisions. This paper describes the rationale and methodology developed for the Proactive Road Safety Program to quantify road safety risk and assign an economic value to justify funding for proactive road safety projects.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".