Assessment of selected methods of estimating the maximum back-of-queue size on a signal-controlled intersection approach
Bibliographic record
Abstract
The article presents the characteristics and evaluation of the accuracy of estimating the maximum length of the queue of vehicles at signalized intersections by commonly used methods in the world. The analyzes were based on the latest editions of the guidelines in the United States, Canada, Australia, Germany and Poland. In order to carry out accuracy analyzes, traffic tests were carried out at 5 intersection inlets in three different cities in Poland (Bydgoszcz, Torun, Warsaw), covering all phases of vehicle queue formation during individual periods of the signaling cycle (effective red and green signal). In total, the analysis had the results of tests from 81 hours of observation ~23,000 behaviors of vehicle drivers. Based on the analyzes it was found in particular: (1) slight differences in the construction of mathematical models of the considered calculation methods, with the exception of the US HCM model from 2016; (2) small errors in estimating the maximum queue length in unsaturated vehicle flow states (~3-5 vehicles/cycle and ~2-6 vehicles/15 minutes interval); (3) quite large errors in estimating the maximum queue length in saturated and oversaturated vehicle flow states (~11-16 vehicles/cycle and ~15-18 vehicles/15 minutes interval); (4) the main impact on estimation errors in oversaturation traffic states is not taking into account or incorrect determination of the 'so-called' initial queue length of the period preceding the analyzed period.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".