Impact of roadside advertisements near traffic signs on driving safety
Bibliographic record
Abstract
A common distraction that affects drivers on the road is billboards. It can substantially impact how well drivers manage their speed, maintain their lanes, focus their attention, and react to stressful situations. When the billboards are placed next to vital traffic signs, they negatively affect drivers, impairing their ability to recognize these signs visually and, more significantly, altering their driving behavior. In this case, the impact of billboards on drivers is mainly influenced by two critical factors: billboard size and relative position to the traffic signs. In this study, a driving simulator was used to study the influence of these two factors on drivers. For this purpose, the eye movement, EEG and driving behavior data of drivers under different combinations of these two factors were collected. The data were analyzed and comprehensively evaluated using a two-factor repeated measure variance analysis and matter-element model. The results provided three main conclusions. First, the driver's ability to recognize signs and driving behavior are significantly influenced by the size of the billboard, and the impact is higher the smaller the billboard is. Second, the relative anteroposterior position of the billboard and the signboard significantly affects how people recognize signs and behave while driving. The driver was more affected by the billboard's placement in front of the signboard. Third, the drivers were impacted by the billboard size and relative anteroposterior position between signs and billboards. However, the influence of the relative anteroposterior position was more than that of the size. Based on the study's findings, some guidelines for billboards to avoid accidents in the actual world can be developed. First, when positioning billboards around significant signs, every attempt should be made to put the billboard behind the sign so that the driver may first finish identifying the sign. Second, to minimize the impact on drivers, a large billboard (8 m x 24 m), placed as far away from the sign, should be used wherever possible. Finally, it is vital to develop uniform regulations and norms for the size standards of billboards in various settings due to the vast variations in billboard sizes used in China.
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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 | 0.005 |
| 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 | 0.001 | 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".