Evaluation of the impact of a public bicycle share program on population bicycling in Vancouver, BC
Bibliographic record
Abstract
Public bicycle share programs have been implemented in cities around the world to encourage bicycling. However, there are limited evaluations of the impact of these programs on bicycling at the population level. This study examined the impact of a public bicycle share program on bicycling amongst residents of Vancouver, BC. Using an online panel, we surveyed a population-based sample of Vancouver residents three times: prior to the implementation of the public bicycle share program (T0, October 2015, n = 1111); in the early phase of implementation (T1, October 2016, n = 995); and one-year post implementation (T2, October 2017, n = 966). We used difference in differences estimation to assess whether there was an increase in bicycling amongst those living and/or working in close proximity (≤500 m) to Vancouver's Mobi by Shaw Go public bicycle share program, compared to those living and working outside this area. Results suggest that only living or only working inside the bicycle share service area was not associated with increases in bicycling at T1 or T2 relative to those outside the service area. Both living and working inside the bicycle share service area was associated with increases in bicycling at T1 (OR: 2.26, 95% CI: 1.07, 4.80), however not at T2 (OR: 1.37, 95% CI: 0.67, 2.83). These findings indicate that the implementation of a public bicycle share program may have a greater effect on bicycling for residents who both live and work within the service area, although this effect may not be sustained over time.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".