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Record W4220930410 · doi:10.1155/2022/3224485

A Study on the Utility Measurements and Influencing Factors of High-Speed Rail and Air Passenger Travel

2022· article· en· W4220930410 on OpenAlexvenueno aff
Peiwen Zhang, Wenke Zhao, Yu Wang, Minke Wang

Bibliographic record

VenueJournal of Advanced Transportation · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesNational Science Foundation
KeywordsAir travelTransport engineeringTravel behaviorPreferenceBusinessChinaRange (aeronautics)Per capitaGeographyAviationEconomicsEngineeringMicroeconomicsDemography

Abstract

fetched live from OpenAlex

The increase in travel mode options has led to changes in the travel decision-making behaviors of passengers and differences in spatial patterns of the markets of high-speed rail and air travel. Taking China’s central cities as the research subject, we analyze the spatial differentiation characteristics of high-speed rail and air transportation markets from a geographic perspective based on the passenger travel utility function. We analyze the influence on passenger travel decision-making behavior from the perspectives of the socioeconomic level and fare structure. The findings show that in the central city transportation market, passengers have a stronger preference for air travel. However, there are differences between regions, with high-speed rail dominating more in the partial north and air focusing on the partial south. As the value of time per capita increases, the dominant range of air travel gradually increases, while the dominant range of high-speed rail travel is compressed to some extent. An increase in fares does not cause a significant decrease in air demand; however, a reduction in fares leads to an increase in air passenger travel satisfaction.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.246

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 communication0.0000.000
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 head0.071
GPT teacher head0.254
Teacher spread0.182 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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