North American Carsharing: A Ten Year Retrospective
Bibliographic record
Abstract
Carsharing (or short-term auto use) organizations provide members access to a fleet of shared vehicles on an hourly basis, reducing the need for private vehicle ownership. This paper reflects a ten-year retrospective of carsharing in Canada and the United States (U.S.), including results from a 2008 operator survey. Since 1994, a total of 50 carsharing programs have been deployed in North AmericaØ33 are operational, and 17 are defunct. As of July 1, 2008, there are 14 active programs in Canada and 19 in the U.S., with approximately 319,000 carsharing members sharing over 7,500 vehicles in North America. Another six programs are planned to launch in North America by January 2009. The four largest providers in the U.S. and Canada support 99% and 95.2% of total membership, respectively.In this ten-year retrospective, the authors examine North America's carsharing evolution from initial market entry and experimentation (1994 to mid-2002) to growth and market diversification (mid-2002 to late-2007) to commercial mainstreaming (late-2007 to present). This evolution includes increased competition, new market entrants, program consolidation, increased market diversification, capital investment, technological advancement, and greater inter-operator collaboration. Ongoing growth and competition are forecasted. Rising fuel costs and increased climate change awareness will likely facilitate this expansion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".