The Issue of Urban Transport Planning in Saudi Arabia: Concepts and Future Challenges
Bibliographic record
Abstract
Sustainable mobility is a growing field that allows researchers to pay attention to the problem of public transit and its constraints. In spite of this, many developing countries often overlook this aspect of the question and focus on monetary issues instead. This causes an imbalance between a variety of sources of impact, such as the local economy, environmental problems, and even social interactions. The case of Riyadh, one of the essential transports and financial arteries of Saudi Arabia, was important because it showed that the city administration is yet to invest more resources in the existing Metropolitan Transportation Plan (MTP) and improve other non-quantifiable factors. The method applied for this research was a detailed review of the current plan that was completed in an attempt to highlight the biggest weaknesses and identify the opportunities to capitalize the future local transport planning. It was proposed to implement the Social Impact Assessment (SIA) method and formulate clearer objectives regarding how metro and bus stations should be located and maintained in order to make it easier for the citizens of Riyadh to reach all necessary destinations. A renewed framework is proposed to help the city administration cope with the increasing agglomeration and unemployment rates.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".