Carsharing and Station Cars in Asia
Bibliographic record
Abstract
In recent years, there has been significant worldwide activity in shared-use vehicle systems (i.e., carsharing and station cars). Much of this activity is taking place in Europe and North America; however, there has also been significant activity in Asia, primarily in Japan and Singapore, with some planned activity in Malaysia. The latest shared-use vehicle system activities in Japan and Singapore are examined, beginning with a historical review followed by an evaluation of their current systems. Overall, there are several well-established systems in Japan (18 systems having approximately 176 vehicles and 3,500 members) and Singapore (four systems having approximately 432 vehicles and 12,200 members). A new program was planned to launch in spring 2006 in Kuala Lumpur, Malaysia, with 10 vehicles. In contrast to most European and North American cities, Japan and Singapore already have a wide range of viable public transportation modes. The primary carsharing focus in Japan is on business use, and in Singapore, on residential–neighborhood use. This likely is because of limited vehicle licensing and high car-ownership costs in Singapore. Further, systems in Japan and Singapore have a high degree of advanced technology in their systems, making the systems easy to use and to manage. The member–vehicle ratios in Asia appear to be approximately the same as in Europe and Canada and less than in the United States. It is expected that Asian shared-use vehicle systems will continue to have steady growth in number of organizations, vehicles, and users.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".