Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use
Bibliographic record
Abstract
Planning and transportation professionals are promoting a variety of sustainable travel alternatives, such as public transit usage, walking, and cycling, as affordable transportation options to counter the negative effects of widespread car use. In their traditional form, these alternative transport modes do not always offer the flexibility or convenience of the car; therefore, innovative solutions have been developed to allow active and public transport to compete better with the car. Shared bicycle systems have been adopted by a growing number of cities and regions throughout the world, yet little is known about the users of the systems and their motivations. A survey was conducted in Montreal, Quebec, Canada, in the summer of 2010 to determine the factors that encouraged individuals to use the system and the elements that influenced frequency of use. The factor found to have the greatest effect on the likelihood for use of a shared bicycle system was the proximity of home to docking stations. Ownership of a yearly shared bicycle membership was associated with cyclists riding shared bicycles 15 additional times per year. Respondents indicated that they valued the shared bicycle's trendy status and the role that it could play in bicycle theft prevention. The potential of shared bicycle systems can be maximized by increasing the number of docking stations in residential neighborhoods and by emphasizing the popularity of shared bicycles and theft prevention in advertising campaigns.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".