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Record W4406300898 · doi:10.58812/wsshs.v2i12.1512

Impact of Regional Structure and Topography on the Effectiveness of Public Transportation Services

2024· article· en· W4406300898 on OpenAlex
Ilham Ilham

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWest Science Social and Humanities Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsBusinessRegional scienceTransport engineeringGeographyEngineering

Abstract

fetched live from OpenAlex

This study investigates the impact of urban structure and topography on public transportation effectiveness in developing countries. Using structural equation modeling analysis of survey data from 500 public transit users across five diverse cities, we examine how these geographical factors influence accessibility, reliability, comfort, and user satisfaction. Results indicate that both urban structure and topography significantly affect transportation effectiveness, with urban structure having a slightly stronger influence. Polycentric urban designs and moderate topography were associated with higher public transit effectiveness. User perceptions highlighted the importance of integrated planning approaches. The findings suggest policymakers should consider geographical contexts when designing public transportation systems, potentially through adaptive infrastructure and context-specific service models. This research contributes to a more nuanced understanding of public transit planning in varied urban environments of developing nations.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
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 head0.083
GPT teacher head0.347
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