Bibliographic record
Abstract
This article presents an overview of selected forms of so-called shared mobility in cities, along with current trends. It focuses on bicycles, kick scooters and motor scooters. These forms of transport provide a new, unique method of moving around cities. They enhance freedom of movement and are accessible. They are easy to use and users have a positive experience. The growing popularity of these means of transport is reflected in the annual increase in the number of rentals. Usually, they are used to get to work, school, university, office, or a public transport station and back home, as well as for social and recreational purposes. In addition to user safety concerns, the primary challenges for these modes of transport involve infrastructure accessibility, permitted maximum speed, sensitivity to weather conditions, lack of space for luggage or a passenger, battery charging and the choice of the most appropriate business and operational model. The author concludes that the sharing of bicycles, motor scooters, kick scooters and other similar means of transport, i.e. broadly defined micro-mobility, is only at the initial stage of development and, in the coming years, we should expect an even greater demand for these types of urban transportation systems.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".