Barriers, Adoption, and Use of a Bike-Sharing System: A Market-Segment Approach to Current and Potential Users in Montréal, Canada
Bibliographic record
Abstract
Bike-sharing systems have gained considerable traction as a solution to many urban transport challenges. (Note that, in this paper, “bike” means “bicycle,” not “motorbike”.) Limited research has explored the market dynamics driving bike-sharing usage among different population segments. This study is the first to apply a market-segmenting approach to analyze factors influencing both existing and potential users’ adoption of a bike-sharing system in Montréal, Canada. Utilizing a bilingual online survey conducted in spring, 2024, this research investigates the factors that limit or prevent the use of a large-scale bicycle-sharing system—Bixi—among different population groups. This work applies factor and k-means cluster analyses to identify distinct profiles within existing users ( N = 561) and non-users ( N = 763) of Bixi. The findings reveal key barriers faced both by users and non-users, particularly highlighting the need for expanding station availability in underserved areas. These findings address important equity issues. Measures to address cost and membership-related challenges would have the greatest impact on bringing low-income non-users into the system, while improving mobile technology accessibility would have the most significant effect on increasing adoption among younger, low-income groups. These insights can be of interest to policy developers aiming to expand bike-sharing systems service quality, enhance its resilience, and promote more equitable outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".