Stationary Models of Unqueued Freeway Traffic and Some Effects of Freeway Geometry
Bibliographic record
Abstract
Occupancies and flows were jointly sampled from numerous freeway segments in nearly stationary, unqueued traffic. The data from one segment were typically averaged across all lanes there and plotted. Each plot was compared with one sampled at a neighboring freeway segment, with the two segments differing only in their number of travel lanes. Such comparisons were repeated for a total of five pairs of segments on five freeways in and near Toronto, Canada and in California. All occupancy-flow relations were piece-wise linear in form for average flows up to about 2,000 vehicles per hour per lane. Only when traffic became moderately dense did average vehicle speeds diminish with increasing occupancies. The occupancy above which these speed diminutions occurred was the same for both segments in a pair. Notable, however, the average vehicle speed corresponding to a given occupancy was always higher on the segment with the larger number of lanes. The driver psychology that can explain some of these findings is discussed. Findings are also contrasted with information currently provided in traffic handbooks.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".