The impact of pedestrian countdown signals on pedestrian-motor vehicle collisions: a reanalysis of data from a quasi-experimental study
Bibliographic record
Abstract
OBJECTIVE: To perform a more sophisticated analysis of previously published data that advances the understanding of the efficacy of pedestrian countdown signal (PCS) installation on pedestrian-motor vehicle collisions (PMVCs), in the city of Toronto, Canada. METHODS: This is an updated analysis of the same dataset from Camden et al. A quasi-experimental design was used to evaluate the effect of PCS on PMVC. A Poisson regression analysis, using a one-group comparison of PMVC, pre-PCS installation to post-PCS installation was used, controlling for season and temporal effects. The outcome was the frequency of reported PMVC (January 2000-December 2009). Similar models were used to analyse specific types of collisions defined by age of pedestrian, injury severity, and pedestrian and vehicle action. Incidence rate ratios with 95% CI are presented. RESULTS: This analysis included 9262 PMVC, 2760 during or after PCS installation, at 1965 intersections. There was a 26% increase in the rate of collisions, pre to post-PCS installation (incidence rate ratio=1.26, 95% CI 1.11 to 1.42). CONCLUSIONS: The installation of PCS at 1965 signalised intersections in the city of Toronto resulted in an increase in PMVC rates post-PCS installation. PCSs may have an unintended consequence of increasing pedestrian-motor vehicle collisions in some settings.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".