Improved Model of Delay and Measurement Analysis at Signalized Intersection
Why this work is in the frame
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Bibliographic record
Abstract
【Objective】In urban road network,vehicles delay generally approaches 80% at the intersection.Survey and investigation of delay at the intersection play an important role in the practical significance for urban traffic management and control.【Methods】Compared to Australian delay model and Canadian delay model,an improved delay model was proposed in this paper.The delay was measured by the License Number Matching Method when the saturation was closed to 1.And then the measured results were compared with that of the three models【Results】The improved delay model is similar to Australian delay model and Canadian delay model in the case of lower and higher saturation,respectively.When the saturation is close to 1,the improved delay model is better agreement with the measured results and approaches high precision.Compared with Markov chain method proposed by Brilon and Wu,it is so simple,convenient and easy-to-operate for the improved delay model.【Conclusion】The improved model is suitable for analyzing the delay that the urban road intersections are mixing low speed.
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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 it