Analytical Method for Estimating Delays to Vehicles Traversing Single-Lane Roundabouts as a Function of Vehicle and Pedestrian Volumes
Bibliographic record
Abstract
Modern roundabouts that have unsignalized pedestrian crossings typically provide right-of-way to pedestrians, and therefore vehicles entering or exiting the roundabout must yield to pedestrians. The requirement that vehicles seek gaps in the pedestrian stream results in four distinct sources of delay to vehicles traversing the roundabout. Existing analytical methods for estimating delays to vehicles entering roundabouts typically consider only one of these four sources and ignore the other three. This paper presents an analytical model for estimating delays to vehicles traversing a single-lane roundabout. The model is based on gap acceptance and queuing models and explicitly estimates delays for each of the four sources for each origin–destination movement in the roundabout. The proposed model is evaluated for a typical single-lane roundabout by comparing the model estimates with estimates obtained from the VISSIM simulation model and the Highway Capacity Manual (HCM) method for a range of traffic and pedestrian volumes. The results of this evaluation demonstrate that the proposed model is superior to the HCM method and provides delay estimates that are comparable with those obtained from microsimulation modeling. Further research is recommended to validate the model results by using field data.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".