Closing the Cycling Gap: Examining Equity Implications of Montreal’s Bikesharing Network Growth
Bibliographic record
Abstract
The worldwide growth of bike-sharing systems demonstrates their mass appeal and success. Montreal’s bike-sharing system, BIXI, opened in 2009, is now one of the largest in the world, and has drastically expanded since being taken over by the City of Montreal in 2014. The system now has over 750 stations up from 459 in 2014. This paper analyzes BIXI’s expansion through the lens of equity, revealing how factors including race and income can explain its growth. First, we mapped high-need equity areas that BIXI could have plausibly expanded to, and those that BIXI actually expanded to. We then created a logistic regression model with dissemination areas that gained new access to BIXI as the dependent variable, and income, race, the number of nearby trips, the population count, and the number of bus stops as independent explanatory variables, and controlled for distance from existing stations. Our regression findings demonstrated that race and income were statistically significant in explaining BIXI’s expansion, with a general trend that BIXI was expanding into lower-income neighborhoods and neighborhoods with a greater proportion of visible minorities, as defined by Statistics Canada. Finally, we compared the same explanatory variables in areas that had gained access to BIXI stations, and those in areas that had not but plausibly could have to show the socioeconomic differences in areas gaining new access to service.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".