Predicting Operating Speeds at Urban Multilane Roundabouts in Abu Dhabi, United Arab Emirates
Bibliographic record
Abstract
There are more than 460 multilane roundabouts located in Abu Dhabi, the capital city of the United Arab Emirates, and its surrounding areas. Most of those roundabouts have three entry/circulatory/exit lanes with large radii, which resulted in the majority of drivers exceeding the speed limits for those roundabouts. Those excessive operating speeds, along with lack of drivers’ awareness of the proper rules of driving at roundabouts, have resulted in increased collision frequencies at Abu Dhabi roundabouts. In this paper, operating speeds were measured at 12 roundabouts in Abu Dhabi and those collected speed observations were used to calibrate regression models to predict the 85th percentile operating speeds at roundabouts in Abu Dhabi. Predicting operating speed at a roundabout, during its design stage, is necessary to ensure that the expected operating speed and capacity will meet the design expectations. Three models were calibrated to predict the entry, circulating, and exit speeds, respectively. The calibrated models were validated with data not used in calibration and they were found to be stable and robust. The findings of this research study will help engineers when designing new roundabouts in Abu Dhabi or other cities with similar characteristics. This research study also provides a methodological framework for other researchers when conducting similar speed studies for roundabouts in other cities or metropolitan areas around the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".