Influence of signal countdown timer on efficiency and safety at signalized intersections
Bibliographic record
Abstract
Signal countdown timers (SCTs) are expected to help drivers with better decision-making processes. Because of the inconsistent outcomes from previous studies associated with them, the present study evaluates the influence of SCTs on intersection efficiency and safety by conducting “before and after” study at three signalized intersections located in New Delhi. A methodology is proposed to estimate the start-up lost time at signalized intersections under highly heterogeneous condition. Green signal countdown timer was found to have no influence on saturation flow, though its red component helped reduce the start-up lost time significantly. These two components also affect the red light violations at the intersection locations — by increasing violations during the last 10 s red and decreasing them during the initial 10 s of red. Timer-off scenario enlarged total ranges of both Type I and II dilemma zones at the onset of amber and brought Type II dilemma zone earlier.
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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".