CREATING SAFER URBAN ENVIRONMENTS FOR CYCLISTS IN TORONTO - MRP - SMIRNOVA
Bibliographic record
Abstract
Despite Toronto’s dedication to reach Vision Zero goals since 2016, serious injuries and deaths on the roads of the city, including those while cycling, still happen in 2023. The need to prevent serious injuries and deaths of cyclists in Toronto and many other cities in North America is evident. However, research on cyclist safety in the North American context is limited. Therefore, we conducted a study to see which urban environments in Toronto are associated with high injury risks for cyclists and which improvements in the built environment can increase the safety of these vulnerable road users. This study uses the Killed and Seriously Injured dataset provided by Toronto police and the city-wide crowd-sourced cyclist volume dataset provided by Strava Metro to identify hot spots of cyclist injuries and determine the risk to individual cyclists in each of the identified hotspots. Based on these findings, eight types of urban environments in Toronto were identified by K-means clustering analysis and the risks associated with each class were analysed. Based on scientific literature and our analysis, recommendations for improvement for the different types of built environments in Toronto were proposed in the discussion section of this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".