Modeling Freeway Diverging Behavior on Deceleration Lanes
Bibliographic record
Abstract
Typically, deceleration speed-change lanes (SCLs) and exit ramp terminals on freeways are designed to allow vehicles to depart the freeway safely and efficiently. Freeway diverge areas with adequate SCL lengths should enable the exiting drivers to diverge off the freeway through traffic, decelerate to the desired ramp speed, and exit the freeway safely and comfortably. Safety problems can be expected if drivers are forced to reduce speed on the main traffic lanes or to decelerate at a very high rate. In Canada and the United States, the current design guides suggest that operation and safety could be enhanced on freeway SCLs by increasing their length. In this study, driver speed behavior at freeway diverge areas was examined by using data collected on 13 exit ramp terminals on Highway 417 in the city of Ottawa, Canada. The speed, geometric, and traffic data were collected and employed to model 85th percentile speeds and deceleration rates using linear regression analysis. The modeling attempts integrated in this study resulted in eight statistically significant predictive models at the 5% level of significance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".