Site selection process and methodology for the deployment of intersection safety cameras in British Columbia
Bibliographic record
Abstract
The Intersection Safety Camera Program (ISCP) in British Columbia (BC), Canada has been proven to be effective in reducing the frequency of collisions at locations where the intersection safety cameras (also known as ‘red light cameras’) have been deployed. Post-implementation evaluations of the ISCP conducted by the Insurance Corporation of British Columbia indicated that there was a 14% reduction in injury collisions 18 months after the program was implemented. Later, a follow-up study examined the safety performance 36 months after ISCP implementation, which indicated that the injury collisions were reduced by 6.4%. Given the on-going and long-term success of the ISCP at reducing collisions, it was decided that the program should be expanded. To support ISCP expansion, it was necessary to examine how the program had been implemented and to learn from the results of the previous program evaluations. A critical element of the ISCP is the selection of sites to be targeted for intersection safety camera deployment. The selected sites should have a demonstrated safety problem, such that the site will offer significant potential for improvement after an intersection safety camera has been implemented. In addition, sites should be selected such that the life-cycle cost of the intersection safety camera deployment will be less than the safety benefits that will be accrued in terms of reduced collisions and the associated collision costs. This paper presents the process and methodology that were used to select candidate sites for the deployment of an expanded ISCP in British Columbia.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".