Public Bikesharing in North America: Early Operator Understanding and Emerging Trends
Bibliographic record
Abstract
Public bikesharingâthe shared use of a bicycle fleet by the publicâis an innovative mobility strategy that has recently emerged in major North American cities. Bikesharing systems typically position bicycles throughout an urban environment, among a network of docking stations, for immediate access. Approximately five years ago, information technology (or IT)-based bikesharing services began to emerge in North America. Between 2007 and March 2013, 28 IT-based programs have been deployedâ24 are operational, two are temporarily suspended, and two are now defunct in the United States (U.S.) and Canada. Bikesharing growth potential in North America is examined on the basis of a survey of all 15 IT-based public bikesharing systems operating in the U.S. and all four programs deployed in Canada, as of January 2012. These programs accounted for 172,070 users and 5,238 bicycles and 44,352 users and 6,235 bicycles in the U.S. and Canada, respectively, in January 2012. This paper reviews early operator understanding of North American public bikesharing and discusses emerging trends for prospective program start-ups.Public bikesharingâthe shared use of a bicycle fleet by the publicâis an innovative mobility strategy that has recently emerged in major North American cities. Bikesharing systems typically position bicycles throughout an urban environment, among a network of docking stations, for immediate access. Approximately five years ago, information technology (or IT)-based bikesharing services began to emerge in North America. Between 2007 and March 2013, 28 IT-based programs have been deployedâ24 are operational, two are temporarily suspended, and two are now defunct in the United States (U.S.) and Canada. Bikesharing growth potential in North America is examined on the basis of a survey of all 15 IT-based public bikesharing systems operating in the U.S. and all four programs deployed in Canada, as of January 2012. These programs accounted for 172,070 users and 5,238 bicycles and 44,352 users and 6,235 bicycles in the U.S. and Canada, respectively, in January 2012. This paper reviews early operator understanding of North American public bikesharing and discusses emerging trends for prospective program start-ups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".