Bibliometric analysis of carsharing and car rental research in the field of urban transportation and tourism transportation
Bibliographic record
Abstract
This paper reviews recent carsharing and car rental research bibliometrically. The study examines the evolution, structure, and boundaries of Web of Science-reviewed carsharing and car rental research. VOSviewer and SPSS 22 evaluated 204 vehicle rental and 574 carsharing articles in the WOS core collection. A gradual rise in car rental and carsharing studies is shown. China and the US produce the most carsharing and rental publications, respectively. China is the most productive country for carsharing publications and the United States for car rental publications. While China and the United States cooperate on carsharing, the United States cooperates with other countries (Canada, Germany, England, France, Australia, Portugal, Taiwan, Israel) on car rental. Co-occurrence network analysis shows that carsharing has five main themes: sharing economy, electric vehicles, transportation, shared mobility, and mobility as a service, while car rental research has four main themes: revenue management, transportation, quality service, and e-commerce. Carsharing and car rental studies share transportation themes. Carsharing subjects include transportation, engineering, business economics, environmental science ecology, science technology, and computer science, while car rental subjects include management, operations research, economics, transportation, business, transportation science technology, business finance, engineering, tourism, and environmental science. Car rental concerns vary by management, tourism, and finance. Tourism literature neglects car hire. This study thoroughly reviews 26 years of automobile rental and 22 years of carsharing literature. Thus, it can help academics comprehend automobile rental and carsharing studies and direct future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.244 | 0.309 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".