Probabilistic Analysis and Design of Freeway Deceleration Speed Change Lanes
Bibliographic record
Abstract
In highway design, knowledge about the design parameters and inputs is imperfect.Current geometric design guides provide deterministic methods for the design requirements by using conservative values to consider uncertainty.The design of freeway deceleration speed change lanes (SCLs) depends on the manner of deceleration, initial speed, and final speed at the SCL.SCL length should provide drivers with enough distance to diverge at a reasonable speed and decelerate comfortably.The purpose of this research is to develop probabilistic methodology for evaluating and designing freeway deceleration SCLs using reliability analysis.Models were developed to evaluate the operational performance of SCLs using field data.Three different methodologies were used for evaluating SCL length.PNC, which corresponds to the probability that drivers require a deceleration length longer than what is provided at the SCL, was calculated for each study site.Design graphs were developed to design based on PNC for lengths below 300 m.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".