The implications of automobile dependency in Abu Dhabi city
Bibliographic record
Abstract
Global trends indicate that automobile dependency is increasing at a tremendous pace especially in developing countries -much faster than the provision of roadway and transport infrastructure.Furthermore, research shows both car use and ownership tend to increase with economic development and growth.Abu Dhabi City is a typical example of a fast growing (economically, population and wealth) city, where car ownership is growing at an annual rate of 24% and most journeys are made by car.Transport policy makers in Abu Dhabi face an uphill challenge as they try on the one hand to develop a comprehensive multi-modal transport network that includes various elements of mass transit systems and on the other, to deal with an increasing car dependency.The externalities associated with a car dependent society is currently being felt in Abu Dhabi and the region in general, with the rise of congestion, health problems associated with lack of physical mobility, accidents and environment deterioration.This paper assesses the impacts of automobile dependency in Abu Dhabi city, and includes how Abu Dhabi compares with similar international cities, outlines key challenges facing Abu Dhabi while taking into account the unique characteristics of Abu Dhabi; and finally concludes with key recommendations that Abu Dhabi can employ to overcome automobile dependency, in order to realize the long-term aspirations of a world-class city with a well-integrated multi-modal transport system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".