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Record W2072451423 · doi:10.5539/jsd.v5n10p68

Mode-Based Transport Sustainability: A Comparative Study of Taipei and Kaohsiung Cities

2012· article· en· W2072451423 on OpenAlexvenueno aff
Tzay‐An Shiau, Quan-Kai Peng

Bibliographic record

VenueJournal of Sustainable Development · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityMode (computer interface)Sustainable transportMode of transportBusinessUrban sustainabilityTransport engineeringEnvironmental scienceEnvironmental economicsPublic transportComputer scienceEngineeringEconomicsEcology

Abstract

fetched live from OpenAlex

Mode shifting is an effective solution to improve transport sustainability. This study developed an indicator system for measuring mode-based transport sustainability at the local level, and the results provided a basis for evaluating the proposed improvement solutions. A case study of Taipei and Kaohsiung confirmed that the mode-based transport sustainability model could highlight which mode provided the most superior transport sustainability and which indicator or aspect could be improved to enhance the transport sustainability of the two cities. The findings from Taipei and Kaohsiung revealed that the use of Mass Rapid Transit (MRT) and bicycles significantly improved transport sustainability, but that the effects of motorcycle use on transport sustainability varied according to local circumstances. Analysis of four transport sustainability improvement scenarios indicated that building bicycle exclusive lanes would have significant environmental and social benefits both in Taipei and Kaohsiung. Increasing parking fees for cars and motorcycles would have a relatively insignificant effect on improving sustainability in Taipei City. Constructing an infrastructure for electric motorcycles in Kaohsiung would provide the most significant improvement to transport sustainability of any of the four scenarios.

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)0.003
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 score0.264
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.319
Teacher spread0.289 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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