A stochastic computational model for yellow time determination and its application
Bibliographic record
Abstract
Summary This paper proposes a stochastic model to determine the yellow time according to the occurring probability of Type‐I dilemma zone ( P DZ ). Unlike the conventional methods generally based on the deterministic traffic flow theory, the proposed model fully accounts for the randomness of input variables such as approaching speed, deceleration rate, perception‐and‐reaction time, and distance to stop‐line at the yellow onset. A theoretical model is firstly established, and a computational program incorporating Monte Carlo Simulation is then developed to facilitate its general solution. These two alternative solution approaches to derive P DZ and Y are proposed, depending upon whether D / V and ( τ + V / 2d ) follow certain analytical distributions or not. In addition, field data at a typical high‐speed highway intersection are collected to validate the model. Based on the validated model, comprehensive sensitivity analysis is conducted to look into the entire picture of the relationship between P DZ and the distributions as well as correlations of the input variables. To demonstrate the application of the proposed model, the required yellow times for various conditions are calculated based on the acceptable levels of P DZ , and representative application tables for typical cases are finally provided. With the aid of the proposed methodology, traffic engineers are capable of designing yellow time in a more sophisticated manner. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".