Technology Scan for Electronic Toll Collection
Bibliographic record
Abstract
The purpose of this project was to identify and assess available technologies and methodologies for electronic toll collection (ETC) and to develop recommendations for the best way(s) to implement toll collection in the Louisville metropolitan area. The intent was to determine which tolling mechanisms maximize efficiency and effectiveness of toll collection while minimizing traffic impacts. This report describes the advantages and disadvantages of tolling, current tolling technologies, the purpose of ETC, and the benefits and costs of ETC. Implementation issues for ETC are discussed, including the location of toll collection facilities, ETC methodologies, interoperability of ETC systems, how to handle vehicles not equipped for ETC, enforcement, pricing strategies, and congestion management. Case studies are presented for the Bay Area Bridges in San Francisco, Highway 407 in Toronto, and the Indiana Toll Road. The study concluded that ETC provides substantial advantages over manual toll collection; ETC technology is proven, accurate, and reliable; interoperability is an important consideration in choosing an ETC technology; the greatest benefits are achieved with open-road tolling; decisions must be made regarding how to deal with non-equipped, non-enrolled vehicles; and adequate enforcement will be critical to the success of any ETC implementation.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".