Estimation of major stream delays with a limited priority merge
Bibliographic record
Abstract
The purpose of this paper is to develop an estimation model for major stream delays at an unsignalized intersection under limited priority conditions. The key idea of this paper is that, when the minor stream drivers accept smaller major stream gaps, some drivers on the major road have to slow down their speeds and adjust their relative positions to avoid traffic accidents, incurring some delays. Assuming that headways in the major stream follow the M3 distribution, the delay for the first major stream vehicle after a limited priority merge is presented using acceptance gap theory and probability theory, and then recursive models are developed for the following major stream vehicles before the next merge. Finally, the precision of the method proposed in this paper is calibrated using field survey data from Changchun city, China, and the results show that the maximum, minimum, and average relative error of the 12 samples are approximately 30.38%, 9.04%, and 18.97%, respectively.
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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".