Evaluating safety effectiveness of roundabouts in Abu Dhabi
Bibliographic record
Abstract
With more than 460 roundabouts located in Abu Dhabi, the capital city of the United Arab Emirates, it is imperative to evaluate the safety benefits provided by those roundabouts. In this study, two approaches were used to evaluate those safety benefits. The first approach is by measuring the 85th percentile operating speeds at a sample of 18 roundabouts in Abu Dhabi to determine whether the measured operating speeds conform to what is recommended by design guides. The second approach is by using a questionnaire to measure how drivers in Abu Dhabi perceive safety when driving at roundabouts and to measure their level of knowledge regarding the rules pertaining to driving at roundabouts. The study found that operating speeds at Abu Dhabi roundabouts typically exceed those recommended by design guides. The study also found that only 4.1% of the drivers interviewed demonstrated a comprehensive understanding of the rules pertaining to driving at roundabouts. Ordinal regression modeling was used to identify driver groups in need for more awareness of the rules to negotiate roundabouts in Abu Dhabi. The study found that the driver group in most need for more awareness is typically young and middle-age male drivers (below the age of 46 years) with less driving experience in countries where roundabouts are common. The questionnaire also revealed that despite the operational benefits provided by roundabouts (in terms of reduced delay), drivers do not prefer to drive at Abu Dhabi roundabouts, which might be explained by drivers' perception of Abu Dhabi roundabouts being not safe.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".