Evaluation of the Rain Effects on Gap Acceptance Behavior at Roundabouts by a Logit Model
Bibliographic record
Abstract
A roundabout is generally known as an efficient, safe, and environmentally friendly intersection. Since 2010, the Korea government has taken the lead in constructing roundabouts as part of a special project. During that time, many ideas have been put forward to improve the safety, operation, and design of such roundabouts. In terms of improvements, it is particularly important to understand roundabout gap acceptance behavior. As such, we investigated gap acceptance behaviors at four roundabouts based on field observation during both good weather and rainy conditions. Based on the observed data, roundabout critical gaps were estimated, and a logit model for gap acceptance using various roundabout variables was developed to investigate gap acceptance maneuvering at roundabouts. A total of 2,421 data events for gap acceptance were collected from the field observation. Out of these events, 64.6% of drivers (1,564 drivers) accepted the given gaps and 35.4% of drivers (857 drivers) rejected them. The values for critical gaps were estimated using several different estimation methods and ranged from 3.3 to 4.7 seconds. The model was developed using four variables including gap size, type of circulating vehicle, traffic volume at the circulating lane, and weather conditions. The developed model shows that a longer gap results in a 3.669 times higher probability of entering roundabouts when the gap is sufficiently great for acceptance than when the gap is smaller. The effects of other variables, such as circulating vehicle types, circulating traffic volume, and weather conditions, are relatively lower than that of a gap size. Rain conditions influenced gap acceptance maneuvering around a roundabout. Drivers need about a 10 percent longer gap to accept entry into roundabouts during rainy conditions, and gap acceptance probabilities are 10 to 20 percent lower for the same given gap time during rainy conditions compared to good weather conditions.
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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 | 0.006 | 0.011 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".