Evaluating new methodologies for estimating performance on two-lane highways
Bibliographic record
Abstract
This paper examines two new methodologies for field estimation of percent time spent following (PTSF) on two-lane highways. Using speed and time headway data, the two methodologies estimate the percentage of vehicles trapped in platoons, unable to pass slow-moving vehicles. A total of 236 h of field data, including individual speeds and headways, were collected at three study sites in the state of Montana. The study examined the sensitivity of PTSF estimates to some important platooning variables. The study also included a comparison of PTSF estimates from the new methods versus those found using the current United States National Research Council Highway capacity manual (HCM) analytical procedures. Study results showed that the two new methodologies were successful in capturing the effect of platooning variables on PTSF estimates and outperformed the HCM procedures in this regard. Further, the results strongly attest to the promise of the proposed methods in advancing the current analytical procedures for estimating performance on two-lane highways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".