Quantification of motorway capacity variation: influence of day type specific variation and capacity drop
Bibliographic record
Abstract
Summary Many road authorities work with static values for road capacities, while it has been proven that capacity is not a fixed quantity. At the same time, there is an increasing need for accurate stochastic input for traffic models, such as the variation in road capacity. In this paper, a methodological framework with a conceptual model for practical stochastic capacity estimation is presented, and a quantification of motorway capacity variation is given for the influence of day‐type specific variations in capacity values. The results of the analysis show that there is a reduction in motorway breakdown capacity of 4% on weekend days in comparison with workdays. Furthermore, a capacity decrease of 8% was found for the discharge capacity in comparison with workdays. The analysis further shows that the breakdown capacity on holidays is not significantly lower than on workdays. Discharge capacity and capacity drops are also derived in each case. The results show that the capacity is significantly different depending on the type of day. A quantification of these differences is given in the form of a Weibull capacity estimation fit for each type‐of‐day scenario. Further consideration of the implications and applications of the framework is also given. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".