Pedaling towards safer streets: evaluating the impact of cycling infrastructure on road safety
Bibliographic record
Abstract
• COVID-19 boosted cycling and infrastructure in Canada, but long-term trends remain unclear. • Cyclist volumes rose with new cycling infrastructure, especially on cycle tracks. • Impact of cycling infrastructure on killed or seriously injured (KSI) per 10km varied by city and infrastructure type. • Vancouver’s painted lanes were associated with higher cyclist KSI; Calgary’s cycle tracks with fewer KSI. • KSIs did not rise despite more riders, especially on cycle tracks, suggesting reduced injury risk per cyclist. Cycling provides health and environmental benefits but poses safety risks. Over the past decade, Canadian cities have expanded cycling infrastructure, such as cycle tracks (physically separated from motor vehicles) and painted lanes, to support safer active transportation. This study evaluates the impact of cycling infrastructure implemented in Toronto, Calgary, and Vancouver. We used infrastructure data (2011–2022) from municipal reports, validated with street view imagery. The primary outcome was killed or seriously injured (KSI) victims per 10 km of road, based on police-reported data (2009–2023). A difference-in-differences design, adapted for gradual infrastructure implementation, was applied using road segments as the unit of analysis. Subgroup analyses examined cyclist KSI and infrastructure type. Crude data on seasonal ridership from intersection counters on a subset of road segments (9 %) were used to estimate post-installation changes. Results varied by city and infrastructure type. In Toronto, no clear associations were found. In Calgary, new cycle tracks were associated with 2.1 fewer cyclist KSIs per 10 km (95 % CI: −4.5 to 0.3). In Vancouver, painted lanes were associated with 4.7 more KSIs per 10 km (95 % CI: 0 to 9.3), and one additional cyclist KSI per 10 km (95 % CI: 0 to 2.1). After implementation, cycle tracks showed substantial increases in cyclist volumes in Toronto (76 %), Calgary (207 %), and Vancouver (732 %), whereas painted lanes exhibited more modest changes. Although low KSI counts introduced uncertainty, cycle tracks were associated with no change or reductions in KSIs, while painted lanes showed no change or slight increases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".