Impact Evaluation of a Public Bicycle Share Program on Cycling: A Case Example of BIXI in Montreal, Quebec
Bibliographic record
Abstract
OBJECTIVES: We examined associations between residential exposure to BIXI (BIcycle-taXI)-a public bicycle share program implemented in Montreal, Quebec, in 2009, which increases accessibility to cycling by making available 5050 bicycles at 405 bicycle docking stations-and likelihood of cycling (BIXI and non-BIXI) in Montreal over the first 2 years of implementation. METHODS: Three population-based samples of adults participated in telephone surveys. Data collection occurred at the launch of the program (spring 2009), and at the end of the first (fall 2009) and second (fall 2010) seasons of implementation. Difference in differences models assessed whether greater cycling was observed for those exposed to BIXI compared with those not exposed at each time point. RESULTS: We observed a greater likelihood of cycling for those exposed to the public bicycle share program after the second season of implementation (odds ratio = 2.86; 95% confidence interval = 1.85, 4.42) after we controlled for weather, built environment, and individual variables. CONCLUSIONS: The implementation of a public bicycle share program can lead to greater likelihood of cycling among persons living in areas where bicycles are made available.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".