Reliability Analysis of Minimum Pedestrian Green Interval for Traffic Signals
Why this work is in the frame
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Bibliographic record
Abstract
The current method of computing the minimum pedestrian green interval for intersection signal timing assumes that the component variables are deterministic. This paper presents a probabilistic method in which the pedestrian start-up time and walking speed are random variables. To establish pedestrian characteristics, data were collected at 14 intersections in downtown, suburban, and tourist areas. The method is based on a safety margin that is defined as the difference between the supplied and demanded green intervals, where the demanded green interval is a random variable. Relationships for the mean and standard deviation of the safety margin of the demanded green interval are developed on the basis of the first-order second-moment analysis. A closed-form solution for the minimum supplied green interval is then derived as a function of the relevant variables, including the vehicular intergreen interval and its component variables. A procedure for establishing the walk and the flashing “don’t walk” intervals is presented. Graphical aids for determining the minimum pedestrian green interval were developed, and application of the proposed method is illustrated using numerical examples. The sensitivity analysis shows that the minimum pedestrian green interval is much more sensitive to the walking speed than the start-up time or their correlation.
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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