Probabilistic Design of Freeway Entrance Speed-Change Lanes considering Acceleration and Gap Acceptance Behavior
Bibliographic record
Abstract
An adequate length of entrance speed-change lanes (SCLs) is required for vehicles’ acceleration and gap-searching purposes so that vehicles can merge onto the freeway comfortably. The current design guides use a deterministic approach for the design of SCL length based only on the acceleration behavior of vehicles in the SCL. This study introduces a probabilistic approach for the design of the length of SCLs that considers both acceleration and gap acceptance behavior of drivers during the merging process. A microscopic simulation, coupled with a Monte Carlo simulation technique, is used to develop a probabilistic model to evaluate the probability of a forced merge by vehicles in the SCL, termed the probability of noncompliance (PNC). Reliability measures can be developed on the basis of the distribution of PNC values for all simulated SCL vehicles at a specific site. Several such measures are estimated for seven study sites and are shown to have high potential to indicate the safety performance at entrance SCL sites. As an example application of the developed model, the mean PNC is estimated to the SCL lengths recommended in North American design guides for a specific freeway design speed but different values of controlling curve design speed and traffic volume in the freeway right lane. The results indicate that the mean PNC may change with the change of either the design speed of the ramp controlling curve or the traffic volume on the freeway right lane.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".