Capacity estimation of unsignalized intersections under heterogeneous traffic conditions
Bibliographic record
Abstract
Capacity of movements at unsignalized intersections are usually estimated based on gap acceptance theory and accuracy of such estimation largely depends on the extent to which its inherent assumptions are satisfied. However, owing to the typical traffic operations at intersections in developing countries, many of these assumptions remain unsatisfied and hence, estimating capacity as per the procedure laid down in the capacity manuals of developed countries will prove inaccurate. The present research focuses on developing the entire procedure for estimating the capacities of movements at unsignalized intersections dealing with heterogeneous traffic. This study is based on data collected from eight different unsignalized intersections located in various parts of India and by using Harders’ capacity model as base, the procedure to estimate the parameters of this model is revised to suit the traffic operations in developing countries and further modifies the Harders’ model using the movement capacities measured in the field.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".