Stationary Models of Unqueued Traffic and Number of Freeway Travel Lanes
Bibliographic record
Abstract
Occupancies and flows were jointly sampled from freeway segments in nearly stationary, unqueued traffic. When plots of occupancy per lane versus flow per lane were normalized by n (the number of travel lanes in the freeway segment from which a data set came), the plots took shapes that were piecewise linear in form (except for conditions that were near capacity) and were clearly influenced by n. Drivers adopted a higher speed (for a given occupancy) while traveling on segments of greater n. Yet, the speeds on these wider segments exhibited greater sensitivity: drivers began decelerating at relatively low occupancies. These findings came from a comparison of a data plot from each of five different freeway segments with the plot from its neighboring segment. Because each segment appeared to differ from its neighbor only in its n, the comparisons (approximately) controlled for other influential factors, including geometric design standards, speed limit, and driver population. The five pairwise comparisons, which verify the reproducibility of the effects of n on the data, were performed for freeways in and near Toronto, Ontario, Canada, and California. The findings are compared with the information currently provided in traffic handbooks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".