Validation of Probabilistic Model for Design of Freeway Entrance Speed Change Lanes
Bibliographic record
Abstract
Traffic operation on entrance speed change lanes (SCLs) is more complicated than on exit SCLs as vehicles interact constantly while merging from the SCL to the freeway. An adequate SCL length gives road users sufficient time to accelerate and to search for a gap so that they can comfortably merge onto the freeway. This study focused on analyzing entrance SCLs from a safety point of view in a probabilistic framework. Sixteen entrance-limited-type SCLs along Highway 417 were considered to investigate the link between the probabilistic measure, probability of noncompliance ( P nc ), and collision frequency. Regression analysis was conducted to quantify the effect of the geometry of the SCL, driver merging behavior along the SCL, road users’ exposure, and P nc on collision occurrence. The model containing P nc outperformed other models, and the inclusion of P nc in the regression models resulted in significant improvement in the model fit. The study provided positive evidence on the validity of P nc as a surrogate measure of safety.
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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 | 0.005 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".