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Record W2563581392

Effects of Air and High-Speed Rail Transport Integration on Profits and Welfare: The Case of Air-Rail Connecting Time

2016· article· en· W2563581392 on OpenAlexaff
Wenyi Xia, Anming Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAir transportIncentiveTransport engineeringEconomic surplusVertical integrationConstraint (computer-aided design)BusinessWelfareSocial WelfareAviationIndustrial organizationAutomotive engineeringEngineeringEconomicsMicroeconomicsMarket economyMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Air-rail integration has become a popular idea to relieve airport congestion and environmental impact of transport industry, especially amid the fast expansion of high-speed rail network around the world. This study examines the circumstances under which air-rail integration can be better justified, by focusing on the effects of reducing air-rail connecting time on transport operators’ profits, consumer surplus, and social welfare. We show that while consumers always benefit from less air-rail connecting time (an integrated hub with seamless transfer between air and rail services is always preferred by passengers), operators of the two modes, air transport and high-speed rail, won’t have an incentive to integrate unless the cost of integration is sufficiently low. Nonetheless, reducing air-rail connecting time enhances total surplus when the hub airport suffers from a certain degree of capacity constraint and the cost of air-rail integration is not too high.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.284

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 head0.204
Teacher spread0.190 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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