Bibliographic record
Abstract
Driverless transit features vehicles or transit units functioning with no onboard intervention from a driver or attendant. In comparison with traditional transit, driverless transit offers reduced labor costs and smaller vehicles, albeit with higher capital costs, and better performance records. As of 2005 there were 37 driverless transit systems in urban service, with five in the United States. Fifteen cities in Canada, Europe, and East Asia had driverless metros. To move driverless transit into the mainstream, research is needed in multiple areas. This paper examines research needs identified in 12 papers presented at the 10th International Conference on Automated People Movers (APMs), with supplemental information from related literature. Integrating APMs into airport security infrastructure, the development of lightweight materials for vehicles and guideways, and personal rapid transit (PRT) reliability and systems theory are among the high-priority research challenges. Medium-priority research needs would focus on special applications of and revenue generators for driverless transit. Although a great deal of driverless transit research has been conducted in simulation, there is a need for operating and performance data from existing systems. These data would enable driverless transit to be considered in modal alternatives analysis. Also, to bring concepts such as PRT into reality, full-scale models need to be tested in operational environments. It is argued that research supported by industry and local agency consortiums can make incremental advancements, but extensive studies require committed, high-level government funding. While the development of driverless transit in the United States has been stifled by high-profile cost overruns and abandonments, successful applications in Europe or elsewhere may revive domestic investment. Potential driverless transit research funding resources are suggested.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".