Prediction of Capacity for Roundabouts Based on Percentages of Trucks in Entry and Circulating Flows
Bibliographic record
Abstract
The objective of this study was to estimate capacity at roundabouts by developing a method to adjust gap acceptance parameters for trucks. Because drivers' gap acceptance behavior is affected not only by trucks in the entry flow but also by trucks in the circulating flow, critical headways were separately estimated for various combinations of vehicle types in the circulating flow at 11 roundabouts in Ontario, Canada; Vermont; and Wisconsin. Because the percentage of trucks was different for different entry legs, the critical headways and follow-up times were estimated at each leg separately. Variations in gap acceptance behavior were also observed at one of the 11 roundabouts for 13 consecutive days to evaluate the statistical significance of differences in behavior between two entry legs. The results showed that a new adjusted critical headway improved the accuracy of capacity estimation and that the critical headways were significantly different between the two legs with different percentages of trucks in the entry flow. The study provided insight into how to capture the effect of trucks on roundabout capacity.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".