Impact of Red-Light Cameras on Traffic Collisions in the City of Ottawa
Bibliographic record
Abstract
The impacts of red-light cameras (RLCs) on overall signalized intersection safety are still debatable.This paper examines the safety impacts of RLCs using actual collision records for treated and untreated signalized intersections in Ottawa, Ontario, Canada.Direct regression analysis of collision data on treated intersections showed a significant impact for RLCs on angle, injury, and fatal collisions but no significant impact on other impact types and severity levels, a finding that was likely affected by the relatively small number of treated sites.On the other hand, an Empirical Bayes before-and-after study showed a significant impact for RLCs, where total and property damage only (PDO) collisions increased while injury and fatal collisions decreased.The impact of RLCs also depended on the collision type, where sideswipe, rear-end, and single motor vehicle collisions increased, but angle collisions decreased at RLC treated sites.It is therefore concluded that RLCs at signalized intersections in the study area reduced severe collisions involving injury or fatality while increasing PDO collisions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".