Quantifying the benefits of peak spreading as a sustainable solution to addressing traffic congestion within the Al Ain private school zone in Abu Dhabi, United Arab Emirates
Bibliographic record
Abstract
Owing to the over-concentration of about 40 schools along an approximately 5km stretch of an urban street within the City of Al Ain, UAE, daily traffic experiences and motorists frustrations manifested in terms of intersection delay, travel time, queues, travel speed and emissions have reached intolerable levels.While varied efforts are being put in place to address the situation, the Department of Transport (DoT) embarked on a pilot study that involved staggering of the starting time of schools over a time interval of 30 minutes for two weeks.The main objective of the study was to examine the effect of peak spreading on minimizing roadway congestion and other externalities within the private school zone.The study was cross-sectional in nature and involved traffic data collection before the implementation of the staggered school starting times and during the scheme.This purpose of paper therefore summarizes the findings of the scheme in quantifiable attributes of intersection delays, link travel times and traffic volumes intensities; and also formed the basis for recommendations to further address traffic congestion within the private school zone.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".