Capacity Estimations for Type B Weaving Areas Based on Gap Acceptance
Bibliographic record
Abstract
Although weaving areas are one of the major types of highway facilities that have long been investigated by many researchers, the estimation of capacity along weaving areas has not been well researched or validated. Most of the literature concentrates on methods for the estimation of the speeds of weaving and nonweaving vehicles and of level of service (LOS). The 2000 Highway Capacity Manual (HCM) weaving methodology includes methods for the estimation of capacities for weaving segments, which are based on the assumption that the density at capacity is the boundary of LOS E–LOS F, 27 passenger cars/km/lane. The objective was to develop a method for the estimation of the capacities of Type B weaving areas based on gap acceptance and linear optimization. In addition, traffic data were obtained from a site located on the Queen Elizabeth Way in Toronto, Ontario, Canada, and were analyzed to identify capacity. Field estimates of capacity were compared with those resulting from the new methodology and from the 2000 HCM methodology. It was concluded that the proposed methodology provides better estimates of the capacity of the study site than the 2000 HCM methodology does when the results obtained by both methodologies were compared with field observations. The collection of additional data is required to validate the proposed model for a variety of Type B weaving segments and for various traffic and highway design conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".