Toll Demand Model for the Delaware Department of Transportation Travel Demand Model
Bibliographic record
Abstract
As transportation providers across the nation face growing funding shortfalls, they are increasingly looking to tolling as an option to increase revenue. As toll revenue becomes a more important part of the funding for transportation programs, agencies are seeking to determine the potential impacts of more complex arrays of tolling options such as electronic toll collection and frequency-of-use discounts. The transportation planning organizations responsible for providing information to agencies are being expected to produce more reliable forecasts of traffic and revenue that directly reflect the subtleties of these tolling scenarios. To provide this information, the Delaware Department of Transportation (DelDOT) developed an improved toll demand model. This toll demand model was incorporated into the existing nested logit mode choice model. One thing that makes the new toll demand model unique is that it not only incorporates the toll–no-toll choice but also includes an E-ZPass ownership model used for cash–E-ZPass market segmentation. The model allows DelDOT to reduce the amount of “unquantifiable” attributes included in the mode choice bias constants. The model would be readily transferable to other areas that either do not currently have tolling or do not explicitly account for tolling in their existing models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".