Scanning “Eyes” Symbol as Part of the Walk Signal: Examination Across Several Intersection Geometries and Timing Parameters
Bibliographic record
Abstract
The use of animated scanning “eyes” in conjunction with Walk interval was examined by a series of studies. In the first experiment, conflicts that involved either the pedestrian or the motorist taking evasive action were examined before and after the scanning eyes were introduced at the following locations: two intersections with one-way traffic on both streets, four intersections with two-way traffic on both streets, and two intersections with one-way traffic on one street and two-way traffic on the other. Conflicts were reduced at crosswalks on all eight streets; reductions on seven of the eight streets were significant. The second experiment examined whether it was better to have the eyes look in both directions (eyes scanning back and forth with equal dwell times in each direction) or only in the direction of the threat (unequal dwell times with the eyes looking longer in the direction of the threat at crosswalks on one-way streets). Results showed that looking one way was no more effective than looking both ways. The effectiveness of steady versus intermittently applied scanning eyes was examined in the second experiment and in the third. The results of this study show that a steady scanning eyes display applied in conjunction with the Walk interval was no more effective than an intermittently applied scanning eyes display, with the eyes alternately on for 3.5 s and off for 3.5 s, but was more effective than an intermittent scanning eyes display that was alternately on for 3.5 s and off for 7 s.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".