Public Bikesharing and Modal Shift Behavior: A Comparative Study of Early Bikesharing Systems in North America
Bibliographic record
Abstract
Public bikesharing-the shared use of a bicycle fleet by the public-is 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.This paper discusses the modal shift that results from individuals participating in four public bikesharing systems in North America.The authors conducted an online survey (n =10,661 total sample), between November 2011 and January 2012, with members of four major bikesharing organizations (located in Montreal, Toronto, the Twin Cities, and Washington D.C.) and collected information regarding travel-behavior changes, focusing on modal shift, as well as public bikesharing perceptions.The survey probed member perceptions about bikesharing and found that a majority in the surveyed cities felt that bikesharing was an enhancement to public transportation and improved transit connectivity.With respect to modal shift, the results suggest that bikesharing generally draws from all travel modes.Three of the four largest cities in the study exhibited declines in bus and rail usage as a result of bikesharing.For example, 50% of respondents in Montreal reported reducing rail use, while 44% and 48% reported similar shifts in Toronto and Washington D.C., respectively.However, within those same cities, 27% to 40% of respondents reported using public transit in conjunction with bikesharing to make trips previously completed by automobile.In the Twin Cities, the dynamic was different, as 15% of respondents reported increasing rail usage versus only 3% who noted a decrease in rail use.In all cities, bikesharing resulted in a considerable decline in personal driving and taxi use, suggesting that public bikesharing is reducing urban transportation emissions, while at the same time freeing capacity of bus and rail networks within large cities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".