A Method for Traffic Calming Using Radio Transmitter
Bibliographic record
Abstract
Excessive speed is one of the major factors in transportation safety assessment and is influenced by road geometric design, vehicle properties, and driving pattern. Exploiting innovative methods along with the conventional ones has been proven to be effective in road safety to control the speed limit. In this study, a supplementary tool is introduced to help drivers to observe the speed limit. In line with this objective, the effectiveness of Voice Sign System (VSS) as an Intelligent Transportation System is evaluated. In order to measure the speed, video processing software is used. Moreover, SPSS is utilized to perform statistical analysis and compare upstream and downstream speeds before and after a message transmission. The study is conducted in Yadegar-e-Imam expressway, located in Tehran, to evaluate the effectiveness of a voice message. The message reads as follows: “Observe the Speed Limit.” The results reveal that VSS is an effective tool for speed reduction as well as the speed limit observation in expressways. The study shows that the speed is reduced by 18% on average, and speed limit observation is improved by 46.5%, out of which 18.2% is direct and 28.3% is indirect influence of VSS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".