Who are the ‘super-users’ of public bike share? An analysis of public bike share members in Vancouver, BC
Bibliographic record
Abstract
Public bike share programs have been critiqued for serving those who already bicycle, or more well-off individuals who already have a multitude of transportation options. While substantial research focuses on characteristics of public bike share members, it often overlooks their intensity of use which may relate more directly to transport and health gains. In this study we link system data with member survey data to characterize "super-users" of Vancouver's public bike share system. We used system data from September 1, 2016-August 31, 2017 to calculate member-specific trip rates (trips/month). We linked system data to demographic and travel data for members who completed an online survey in 2017 (1232 members who had made 89,945 trips). We defined super-users as those who made 20 or more trips/month. We used a logistic regression to model demographic and travel characteristics associated with super-users as compared to regular users. Of the 1232 members, 204 were super-users. Super-users made 47% of the trips and had a median trip rate of 29.3 trips/month. In adjusted models, super-users were more likely to be young, male, have household incomes below $75,000, and live and work near bike share docking stations. Super-users had fewer transportation options than regular users, with lower odds of having a personal bike or car share membership. Amongst members, we found a distinct demographic profile for super-users relative to regular users, suggesting that usage is an important consideration when quantifying transport and health gains, and the resulting equity implications of public bike share programs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".