Vancouver's Bicycle Lanes: Retrofitting Arterial Streets to Accommodate Cyclists
Bibliographic record
Abstract
The City of Vancouver has been developing facilities for cyclists since the mid 1980s. However, the development of a more comprehensive bicycle network began in earnest in the mid 1990s when cycling was reaffirmed as one of the City's priority transportation modes. Since that time, the network has grown to incorporate almost 400 lane-kilometers of designated bicycle facilities of varying types. Although the majority of the City's facilities are local street bikeways on minor streets, a network of bicycle lanes on arterial streets has also emerged over the past decade. As with most transportation challenges, the provision of bicycle lanes in a highly developed urban environment cannot be achieved with a one-size-fits-all solution. The constraints posed by Vancouver's relatively narrow street rights-of-way has required creativity to retrofit bicycle lanes where road capacity, parking demands and pedestrian space are often at a premium. Transit operations and goods movement are also important design considerations that are potentially affected by the implementation of bicycle lanes. This paper will explore the various design approaches that have been used to implement bike lanes in Vancouver, and will provide general commentary on the City's experiences with bike lane design to date.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".