Empirical Estimation of Capacity for Roundabouts Using Adjusted Gap-Acceptance Parameters for Trucks
Bibliographic record
Abstract
This study examines the effect of heavy vehicles (trucks) on the entry capacity of roundabouts. Vehicle movements were observed at 11 roundabouts in Vermont, Wisconsin, and Ontario, Canada, and gap-acceptance parameters were estimated for cars and trucks separately. Consistent with previous studies, it was found that the critical headway and the follow-up time were longer for trucks than for cars. It was also found that the follow-up times for truck-involved vehicle-following cases were associated with the central island diameter and the entry angle. The gap-acceptance parameters for all entering vehicles were adjusted to a volume-weighted average of the gap-acceptance parameters for cars and trucks. The capacity was estimated with the existing capacity models with the adjusted gap-acceptance parameters and compared with the observed capacity at three roundabouts. It was found that the rate of reduction in the observed capacity with an increase in the circulating flow was lower at the roundabouts with a higher truck percentage. Also, the capacity models with the adjusted gap-acceptance parameters estimated the capacity more accurately than did the models with the unadjusted parameters. The study underscores the importance of considering the effect of trucks on capacity for the roundabouts with a high truck volume.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".