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Record W4200367669 · doi:10.18280/ijsdp.160712

The Issue of Urban Transport Planning in Saudi Arabia: Concepts and Future Challenges

2021· article· en· W4200367669 on OpenAlexvenueno aff
Khalid Mohammed Almatar, Abdulaziz I. Almulhim

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaVariety (cybernetics)Plan (archaeology)BusinessDestinationsEnvironmental planningOrder (exchange)Public transportUnemploymentTransport engineeringEconomic growthFinanceEngineeringEconomicsComputer scienceTourismPolitical scienceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.277
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sustainable Development and PlanningSame topicEnvironmental and Social Impact AssessmentsFrench-language works237,207