Bibliographic record
Abstract
This paper will discuss how the Washington State Department of Transportation (WSDOT) conducted a congestion relief analysis for the State’s three major urban areas: Central Puget Sound, Spokane and Vancouver during the 2003-2004 timeframe. Phase 1 of the analysis examined a variety of congestion relief scenarios ranging from highway, transit, and roadway value pricing strategies. This phase concluded that value pricing is a very effective strategy in reducing congestion and that a strategic combination of transportation supply and demand management, particularly value pricing should be given greater attention in the future. In 2005, a Phase 2 study was initiated to further examine how pricing, in the form of a network of high occupancy toll (HOT) lane facilities, could affect travel patterns and congestion within the Puget Sound region. Three HOT lane scenarios were tested using the Puget Sound Regional Council’s travel demand forecasting model. The first scenario considered converting all of the region’s 200+ miles of existing and planned high occupancy vehicle (HOV) lanes and reversible express facilities into HOT lanes. The second scenario examined the effects of adding a lane to most of the freeways to create a two-lane HOT system. The third scenario blended the high performing HOT lane elements of Scenarios 1 and 2, together with full pricing of I-5 and the cross-lake bridges. A final scenario priced all roadway lanes within the region. Through modeling analysis, it was found that the HOT scenarios produced favorable reductions in delay and travel time, and increases in person throughput, in comparison to the existing HOV-only policy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".