National Congestion Pricing Conference
Bibliographic record
Abstract
On July 9-10, 2013, 114 leaders in congestion pricing, managed lanes, and parking pricing convened in Seattle, WA to discuss recent successes and challenges to advancing congestion pricing in the United States. Much of the discussion centered on the important outcomes of project implementations, especially the Urban Partnership Agreement (UPA)/Congestion Reduction Demonstration (CRD) programs as well as the Value Pricing Pilot Program (VPPP). The primary objective of the conference was raising the awareness, advancing the state-of-the-practice, and identifying the research and technology transfer needs in support of deploying congestion pricing strategies in the United States. Sharing the collective knowledge and experience of presenters and participants, the conference provided input to the Federal Highway Administration (FHWA), Transportation Research Board (TRB) and other research organizations, and implementing transportation agencies. The participants represented state, local and regional jurisdictions from across the United States and Ontario, Canada. Nearly half of the participants came from regional entities and State agencies; including metropolitan planning organizations (MPOs), State departments of transportation (DOT), and tolling and transit agencies. The FHWA coordinated and worked with Washington State DOT (WSDOT) and the TRB Congestion Pricing Committee to make this conference a success. This report briefly summarizes the conference proceedings.\n
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.233 | 0.051 |
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".