Bibliographic record
Abstract
Developments during the 20th century have shown that it is technically and economically feasible to operate high-capacity driverless trains on metros. The experience of the last 15 years shows that they are safe, and that it is time to move on to the next stage. This article gives a brief history of automatic train operation (ATO) on metros, reports on some plans to convert existing metros to ATO, and outlines the case for automation. ATO trials were first conducted during the early 1960s, and London Underground's Victoria Line was the first ATO line to enter commercial service, in 1968, but it still has staff on board its trains. Most driverless train systems operate in a protected environment, such as major airports, but Japan has several of significant length and passenger volume. The article discusses seven driverless systems that are metros, four of which are in France, and one each in Canada, Malaysia, and Taiwan. Several metro administrations, including those in Berlin and Paris, plan to convert existing lines to driverless operation. Driverless trains can be added rapidly during peak periods to handle surges in passenger demand. It is often difficult to convince rail safety regulators that they are safe, but several systems have platform screen doors to prevent accidents to passengers in stations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".