Installation of speed humps and pedestrian-motor vehicle collisions in Toronto, Canada: a quasi-experimental study
Bibliographic record
Abstract
BACKGROUND: Evidence related to the effectiveness of speed humps on reducing pedestrian-motor vehicle collisions (PMVC) has been conflicting. The purpose of this study was to determine the association between speed hump installation and changes in PMVC rates in Toronto, Canada. METHODS: Speed humps were mapped along with police-reported pedestrian collisions from 2000-2011 and built environment roadway characteristics. A quasi-experimental study identified collision counts before and after speed hump installation, modeled using repeated measures Poisson regression adjusted for season and roadway characteristics. Stratified analyses were conducted by age group and injury severity. RESULTS: There were 27,827 PMVC, with 1344 collisions along 409 roadways with speed humps. PMVC incidence rates/meters of road/month decreased after installation of speed humps (IRR 0.78 95 % CI 0.66, 0.91). Winter, collector roads (versus local), pre-amalgamated city centre and increased land use mix were associated with more collisions. The association between speed humps and PMVC rates decreased more for children (IRR 0.57, 95 % CI 0.41, 0.79) than for adults (IRR 0.80, 95 % CI 0.68, 0.95). CONCLUSIONS: Speed humps are an easily replicated method of traffic calming which have a significant protective effect on PMVC on the roadways where they are installed, particularly for children. There is a need for an area-wide analysis to determine the effects of the installation of speed humps to ensure that PMVC are not being displaced to surrounding roadways.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".