Innovative Tools for Presenting Traffic Performance on Congested Highway Corridors
Bibliographic record
Abstract
The Columbia River Crossing project is a bridge, transit and highway improvement project for Interstate 5 between the states of Washington and Oregon. This bi-state mega-project is aimed at improving the mobility, reliability, and accessibility of the I-5 corridor between State Route 500 in Vancouver, Washington and Columbia Boulevard in Portland, Oregon. I-5 supports eight interchanges within a critical five-mile segment that experiences recurrent congestion. Capacity and operational enhancements within this segment could affect upstream and downstream conditions for multiple hours, including during early morning, midday, and evening periods. Therefore, it was crucial that a dependable freeway simulation tool be developed that could be calibrated to multi-hour conditions for all 23 miles, accurately incorporate ramp origin and destination data, predict speeds and congestion during a continuous 16-hour period, and easily convey complex results to decision-makers and the general public. This paper focuses on the research that was undertaken to develop innovative tools to convey complex results to key stakeholders. Through research the project team discovered few tools currently exist to simply present various measures. Therefore, the project team developed speed/congestion profile illustrations and served traffic volume/speed profile illustrations to easily compare how various alternatives perform, and to understand the effects of capacity conditions, the application of tolling, non-peak period traffic operations, reverse commutes, and diversion affects.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".