Typology of Bikeshare Users Combining Bikeshare and Transit
Bibliographic record
Abstract
This study proposes a methodological framework to understand the behavior of bikeshare-metro-bikeshare (BMB) users and assess the complementarity of bikeshare and transit. This analysis was conducted using Montreal’s Bixi bikeshare data collected over an 8-year period. A k-medoid clustering analysis was performed using three variables describing users’ travel behavior: BMB rate, most frequent BMB trip share, and rate of use of different metro stations. It reveals six groups of BMB users: (1) regular commuters, (2) irregular commuters, (3) occasional commuters, (4) mixed users, (5) leisure users, and (6) utility users. Each group’s share of trips is stable over time. BMB users represent an increasing, yet still marginal, share of 1.8% of Bixi’s annual members. The bikeshare segments of BMB trips averaged 1,180 m, with a standard deviation of 830 m. This confirms bikeshare is useful to complete the first and last kilometer of transit trips. Moreover, BMB trips increased with the expansion of Montreal’s bikeshare network to suburban areas serviced by the metro. This study concludes that bikeshare-metro integration allows bikeshare users to cover greater distances and can thus increase both systems’ ridership.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".