Monitoring day and dark traffic collisions in Toronto neighbourhoods with implications for injury reduction and Vision Zero initiatives: A spatial analysis approach
Bibliographic record
Abstract
• Vision Zero policy improves road safety and reduces traffic accidents and injuries. • Shared-component spatial modeling identifies area-specific risks in day and dark. • Space & time analysis identifies area-specific and mean area trends of injuries. • Bayesian probability estimation and GIS show spatiotemporal hotspots & cold spots. • Spatial epidemiology of socioeconomic, deprivation, & marginalization risk factors. The City of Toronto adopted a Vision Zero strategy in 2016 that aims to eliminate deaths and serious injuries from vehicular collisions. The strategy includes policies to improve lighting to reduce collision risks, and past research has suggested lighting as a road safety factor. We apply Bayesian spatial analysis (including Poisson log-normal regression modelling, shared component spatial modelling, and Bayesian spatiotemporal modelling) to publicly available data on traffic collisions where persons are killed or seriously injured (KSI) based on Day/Dark conditions. We assess (1) links between KSI risk and socioeconomic and built environment factors, (2) spatial distributions of relative Day & Dark KSI risk, and (3) area-specific trends in space and time for Day-Dark KSI risk change across Toronto neighbourhoods. Our analysis does not find significant associations between socioeconomic/built environment factors and KSI risk, but we uncover neighbourhoods with heightened Dark KSI risk and pronounced Day-Dark KSI changes compared to Toronto’s mean area trend. Findings highlight the need for increased policy attention for impacts of lighting on collisions and provide insight for focus regions for improved Vision Zero policy development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".