One-Way Carsharing's Evolution and Operator Perspectives from the Americas
Bibliographic record
Abstract
Classic roundtrip carsharing has been documented as a strategy to reduce car ownership and vehicle miles/kilometers traveled in urban areas. The expansion of carsharing and other forms of shared-use mobility have led to a growing interest in understanding the latest models. In recent years, one-way carsharing has gained momentum across the globe with 18 operators providing services in ten countries worldwide. One-way carsharing does not require its users to return the vehicle to the same location from which it was accessed (in contrast to roundtrip carsharing). Users typically pay by the minute versus the hour and do not require a reservation. There are two one-way models: free-floating and station-based. Free-floating carsharing allows vehicles to be picked up and left anywhere within a designated operating area, while station-based requires users to return vehicles to an available station. In Fall 2013, the authors conducted a survey of 26 roundtrip and five one-way carsharing operators in the Americas (U.S., Canada, Mexico, and Brazil) to understand their perspectives on one-way carsharing and its future. Almost 70 % of roundtrip operators viewed one-way carsharing as a complement to roundtrip carsharing, while 19 % viewed it as a competitor. Twelve percent perceived it as both a complement and competitor. Operators noted public transit, smartcard, and electric vehicle integration as key to this model’s expansion. Half of respondents believed one-way and roundtrip carsharing have similar social and environmental impacts. Given limited understanding of its impacts, more research is needed to document the benefits of one-way carsharing and to help inform policymaking and urban mobility.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".