Impact of Video Advertising on Driver Fixation Patterns
Bibliographic record
Abstract
To assess driver distraction because of video advertising signs, eye fixation data were collected from subjects who passed four video advertising signs, three at downtown intersections and one on an urban expressway. On average, drivers glanced at the signs on 45% of the occasions on which the signs were present. When drivers looked, they made 1.9 glances, on average, with an average duration per glance of 0.48 s. The distribution of eye fixations on intersection approaches where video signs were visible was compared with that on approaches on which video signs were not visible. No significant differences were found in the number of glances made at traffic signals or street signs. On the video approach, a greater proportion of glances were made at the speedometer and rearview mirrors. Glances were made at short headways and occasionally in unsafe circumstances. In the downtown area, glances at static commercial signs were made at larger angles and at shorter headways than was the case for video signs. A comparison of the results with those of other studies showed that video signs were less likely to be looked at than traffic signs (about half the time versus virtually every time, respectively) and that individual average glance durations and total durations were similar to those found for traffic signs in rural environments. These results apply to particular video signs in particular environments. Another on-road study indicates that a video sign on a curve that is close to the line of sight and visible for an extensive period is particularly distracting.
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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 | 0.001 | 0.007 |
| 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) | 0.002 | 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".