Deterministic and Stochastic Freeway Capacity Analysis Based on Weather Conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a fundamental diagram is calibrated for observed traffic data on a freeway segment using triangular regression analysis and the fixed capacity of the freeway is derived. Stochastic capacity analysis is then conducted to investigate the nature of the breakdown phenomenon and its effect on freeway capacity. The Weibull distribution function as a generalized extreme value distribution model is fit to the data. For both deterministic and stochastic capacity analysis, the influence of the weather is evaluated for four types of weather conditions that include clear, rainy, snowy, and low visibility. The statistical analysis results show that weather conditions have a significant effect on both the fixed and stochastic value of freeway capacity. One of the other important findings of this study is that jam density is shown to be significantly affected by weather conditions and needs to be incorporated when developing advanced freeway control and management strategies such as queue detection and management schemes.
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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 it