Traffic Safety Evaluation of Video Advertising Signs
Bibliographic record
Abstract
Road authorities are under increasing pressure from advertisers to allow video advertising in the right-of-way but are understandably concerned about whether video signs constitute a driving hazard. At the City of Toronto's request, a comprehensive assessment of traffic safety impacts related to such signs was carried out in a series of studies involving three downtown intersections and an urban expressway site. An on-road eye fixation study was carried out to determine if drivers look at video advertising signs. Conflict studies were conducted to determine if there were more conflicts on intersection approaches with visible video signs than on those without such signs. A before-and-after sign installation study of headways and speeds on the urban expressway was carried out. Crashes were compared before and after sign installation at the expressway and three intersection sites. Finally, a public survey was conducted to determine if video advertising was perceived to affect traffic safety. On the basis of the eye fixation study and the public survey data, it is apparent that video advertising can distract drivers inappropriately and lead to individual crashes. However, the evidence from other studies was not consistent and suggests that for the particular signs studied, overall impacts on traffic safety are likely to be small. Further studies, especially prospective ones with larger crash data sets, are required to be certain about the findings. A comparison between this study and an earlier one suggests that there are large differences in driver distraction depending on the placement and the environment in which the sign is seen. Further studies are required to determine factors that minimize driver distraction.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".