Selecting Candidate Locations for Red Light Cameras
Bibliographic record
Abstract
Based on the positive findings of an evaluation into their safety effectiveness in August of 2004, the province of Ontario announced that interested municipalities would be able to operate red light cameras. The Region of Durham wished to explore the feasibility of implementing a red light camera program. Particularly, the Region wanted to ensure that the sites were selected in an objective and defensible manner based on sound traffic engineering judgment. This paper discusses the development of site selection criteria, the identification of potential candidate locations using collision data, and further refinement of the list through a detailed office and field review. The candidate locations were selected based on a higher than expected collision performance and an over representation in angle collisions. These locations would have the highest potential for safety improvement, specifically in red light running related collisions. A detailed office and field review was conducted, including a detailed collision analysis, a review of signal operations, intersection layout, traffic signal type and placement, prevailing traffic patterns and operating speeds, and the suitability of each approach for a red light camera. Based on the review, a short list of candidate sites/approaches was developed. For approaches remaining on the short list, it was suggested that the occurrence of red light running be confirmed through a detailed field investigation, a benefit-cost analysis be undertaken to confirm that any alternative treatment identified would not be able to achieve similar results at a lower cost, and rear end collisions be closely monitored in the post-implementation period.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".