Examining safety of electronic signs: using ordinal logistic regression on speeding
Bibliographic record
Abstract
Speeding continued to be of alarming concern for many countries. This paper aims to focus on highlighting speeders characteristics on 40 km/h on a busy urban road with high pedestrian movement. This case study utilised the ordinal logistic regression model to test four predictors. Three of which were age and gender of the driver and the time of day drivers were detected speeding. Whereas the fourth explanatory variable is the ‘period’ of the installation of the signs, which tests the usefulness of electronic signs. The study found that the driver’s age contributes slightly to risky speeding behaviours, and older drivers speed less. Time of the day was found to be significant in the model, with a higher number of TINs being recorded in the afternoon than in the morning. Although gender was not found to be a significant predictor, it was shown to produce results similar to speeding data recorded in Perth roads with males speeding slightly more than females. This difference was more pronounced when higher speeding levels were compared. The period variable in the model relating to the installation of the signs was significant, with drivers slowing down after the installation of the flashing 40 km/h electronic signs compared with before the installations This may prove the usefulness of such signs in reducing speeding behaviour. Hence, reducing harm by reducing frequency and severity of crashes.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".