Site Selection Process and Methodology for Deployment of Intersection Safety Cameras in British Columbia, Canada
Bibliographic record
Abstract
The Intersection Safety Camera Program (ISCP) in British Columbia, Canada, has proved effective in reducing the frequency of collisions at locations where intersection safety cameras (red light cameras) have been deployed. Postimplementation evaluations of ISCP conducted by the Insurance Corporation of British Columbia detected a 14% reduction in collisions resulting in injuries 18 months after the program was implemented. A follow-up study conducted 36 months after ISCP implementation examined the safety performance of ISCP and found that the rate of collisions resulting in injuries was reduced by 6.4%. Given the ongoing and long-term success of 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 ISCP is the selection of sites to be targeted for deployment of intersection safety cameras. The sites selected should have a demonstrated safety problem, such that the site will offer significant potential for improvement after an intersection safety camera has been installed. In addition, sites should be selected such that the life-cycle cost of deployment of the intersection safety camera will be less than the safety benefits that will accrue from reduced numbers of collisions and the associated 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".