Automated speed enforcement reduced vehicle speeds in school zones in Toronto: a prospective quasi-experimental study
Bibliographic record
Abstract
BACKGROUND/AIMS: Vulnerable road user collisions are a leading cause of injury and death. Speed is the direct mechanism for pedestrian injury risk. We evaluate the effectiveness of automated speed enforcement (ASE) at reducing vehicle speeds in school zones. METHODS: Quasi-experimental trial with speeds measured before, during and after ASE implementation. 50 ASE cameras were used at 250 intervention sites in school zones between July 2020 and December 2022. Outcomes were the proportion of vehicles speeding and the 85th percentile vehicle speed. RESULTS: Proportion of vehicles speeding dropped by 45% (RR: 0.55, 95% CI: 0.49, 0.61) and 85th percentile speed dropped by 10.68 km/hour (95% CI: -11.48, -9.88). Reductions in speed were more pronounced at higher speeding thresholds. CONCLUSIONS: A significant reduction in speeding was observed when ASE was implemented in urban school zones.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".