Probabilistic Model for Design of Freeway Acceleration Speed-Change Lanes
Bibliographic record
Abstract
A speed-change lane (SCL) is an auxiliary lane added to the traveled way for the acceleration and deceleration of vehicles entering or leaving a roadway. When the length of an acceleration SCL is adequate, drivers are able to accelerate comfortably from the speed at entrance to a speed appropriate to the road, find a gap in the traffic flow, and merge in a safe and secure manner. The length of an SCL is currently determined in terms of the ramp design speed, the freeway design speed, and the acceleration rate. Embedded in these values are assumptions for the operating speed at the entrance and merging points. This study examined a probabilistic approach instead of such a deterministic approach. The main benefit of a probabilistic approach is that traffic flow characteristics are assumed to be stochastic; therefore, the outcome of a probabilistic methodology is a distribution of drivers’ acceleration distance on the SCL. The reliability-based analysis enables designers to select a specific percentile value of this distribution as a design length that better matches a certain situation and avoids unnecessary extra construction costs. This paper presents analytical and simulation models for the application of the reliability approach, with all parameters based on recently collected field data. Even though the presented model should be superior to the deterministic model adopted in current design guides, additional enhancements are recommended for a full reliability-based, safety-explicit design model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".