Stochastic nature of freeway capacity and its estimation
Bibliographic record
Abstract
The main purpose of this study was to investigate the meaning of freeway capacity, in particular to explore its stochastic nature and to estimate its distribution. The evaluation is based on traffic data from three busy urban freeway sections over 3 full days. Momentary capacity is defined as the intersection of the best-fit regression lines calculated for the dense- and unstable-flow regimes close to the maximum flow. An algorithm was developed for the selection of the relevant pairs for each regression line and is discussed. It is argued that momentary capacity values are stochastic in nature and distributed according to the shifted gamma distribution. Estimation of the parameters of this distribution for the three urban freeway sections is studied. It is proposed that the 5th percentile of the distribution, found to be approximately 2330 vehicles per hour per lane for the prevailing conditions of the basic section, could be adopted as the representative design value of capacity. Another conclusion is that the average distribution of capacities for all three through lanes is relatively close to the distribution of capacities in the middle lane.Key words: freeway flow, capacity, flow breakdown, dense flow, unstable flow.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".