Bibliographic record
Abstract
This article discusses fully automated toll roads, also called cashless toll roads. The author briefly describes a project in Santiago, Chile, that consists of four major expressways which will eventually be the first fully automated urban tollway system. The fully automated toll collection is largely based on transponders (about 90% of users), but nontransponder users can purchase a day-pass. A violator is offered the opportunity to buy a late day pass and if that opportunity is not exercised, is fined $50. The author goes on the describe a similar model used for the past 5 years in Melbourne, Australia; a different style of open-road tolling in Toronto, Canada; and HOT (high occupancy toll) lanes in California, Texas, and Minneapolis. The author then addresses some of the concerns about fully phasing out all toll booths. These include the role of the toll collectors' unions, transponder penetration, transponder standardization, interoperability, and how to handle rural or occasional users. The author concludes with a brief discussion of the benefits of implementing cashless tolling, which include reduced costs, time savings to customers, and reduced emissions output from waiting in line.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.152 | 0.021 |
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".