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Record W4220770854 · doi:10.1155/2022/4062132

Defining Equity Criteria for Determining Fare Zones in Integrated Passenger Transport

2022· article· en· W4220770854 on OpenAlexvenueno aff
Denis Šipuš, Borna Abramović, Martina Jakovčić

Bibliographic record

VenueJournal of Advanced Transportation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPublic transportPassenger transportEquity (law)DisadvantageDisadvantagedTransport engineeringPrivate transportBusinessSustainable transportService (business)Transport systemSustainabilityEconomicsComputer scienceMarketingEngineeringEconomic growth

Abstract

fetched live from OpenAlex

Fare system models in public transport are researched based on the fact that they represent a direct and flexible instrument of influencing passenger behavior and covering public transport costs, which contributes to the sustainability of public passenger transport. Integrated passenger transport, as a concept of public transport management that uses a zonal fare system, defines transport service prices within a fare zone. An analysis of existing fare systems reveals that current systems do not offer equitable access for passengers and that the transport service is not available to everyone. To resolve the issue of the transport disadvantage of potential passengers, society and space, fare system models must be changed to provide equity for disadvantaged participants of the system. This would do away with transport and social disadvantage in the analyzed region. The aim of this research is to define equity criteria in determining fare zones in integrated passenger transport. This is a precondition for an equitable fare model which would ensure an impartial and fair charge of transport services within zones.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.512

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 studies0.0000.000
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 head0.028
GPT teacher head0.350
Teacher spread0.322 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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