Innovative Mobility: Carsharing Outlook Carsharing Market Overview, Analysis, And Trends.
Bibliographic record
Abstract
In October 2018, carsharing was operating in 47 countries and six continents, with approximately 32 million member ssharing over 198, 000 vehicles. Asia, the largest carsharing region measured by membership, accounted for 71.4% of worldwide membership and 54.4% of global fleets deployed. The world’s second largest carsharing market, Europe, accounted for 21.2% of worldwide members and 30.6% of vehicle fleets. As of October 2018, one-way carsharing accounted for 49.63% of global membership and 42.02% of global fleets deployed (based on data provided through expert interviews). The 2018 global one-way market share represented a 238% increase in membership and a 103% increase in fleets since 2016. In October 2018, roundtrip carsharing accounted for 50.37% and 57.98% of global membership and fleets deployed, respectively. Regionally, Europe had the largest percentage of one-way membership, representing 72.3% of the region’s carsharing membership. Oceania had the greatest percentage of one-way fleets regionally, representing 80.91% of the continent’s carsharing fleets. The number of carsharing countries increased from 46 in 2016 to 47 as of October 2018.Notably, carsharing expanded to Columbia in July 2017.Please note in February 2020, ShareNow discontinued services in North America (Montreal, New York, Seattle, Washington DC, and Vancouver). ShareNow will continue in some European 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".