Evolution of dedicated cycling infrastructure in three Canadian cities: 2009 to 2022
Bibliographic record
Abstract
There has been a recent surge in cycling infrastructure implementation across Canada. Accurate cycling infrastructure implementation data are required to evaluate impact on safety and mobility. The objectives of this paper were to quantify the accuracy of municipal cycling infrastructure classification and cycling infrastructure installation dates, and to examine temporal trends in the implementation of on-road dedicated cycling infrastructure. We undertook this work in three Canadian cities; Vancouver, Calgary, and Toronto, from 2009-2022. Infrastructure classification accuracy was assessed using the Canadian Bikeway Comfort and Safety (Can-BICS) system, and implementation dates were verified using GoogleTM historical street view imagery and grey literature. Temporal trends in cycling infrastructure implementation were documented using the verified data. There were discrepancies in infrastructure classification resulting in underreporting in Toronto (8.8%) and Vancouver (8.3%) and overreporting in Calgary (1.7%). The most common misclassification was the municipality reporting the presence of a cycle track, when the infrastructure was a buffered painted lane. Agreement between municipal data and verified installation dates was only 42% in Calgary, 75% in Toronto, and 83% in Vancouver. Infrastructure doubled from 19.2 to 34.2 km/1,000 km roads in Vancouver, 18.7 to 40.2/1,000 km in Toronto, and 1.2 to 11/1000 km in Calgary over the study period. There was accelerated infrastructure implementation in Calgary and Toronto related to the onset of the COVID-19 pandemic. This study highlights and addresses data gaps necessary to examine accurate trends in implementation and to enable future longitudinal studies related to the impact of infrastructure on safety and mobility
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".