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Record W1939152881 · doi:10.1002/atr.1253

Incentive subsidy scheme design with elastic transport demand

2013· article· en· W1939152881 on OpenAlexvenueno aff
Wenqian Zou, Shoshi Mizokami

Bibliographic record

VenueJournal of Advanced Transportation · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSubsidyIncentivePrice elasticity of demandPublic transportMaximizationProfit maximizationScheme (mathematics)Service (business)Profit (economics)Computer scienceOperations researchTransport engineeringEconomicsMicroeconomicsBusinessEngineeringMathematicsMarketing

Abstract

fetched live from OpenAlex

SUMMARY Huge public transport subsidies caused by deficits have become a heavy financial burden on some local governments due to the decline of bus passenger numbers. It is essential to apply the performance‐based contract to bus services considering maximization of social welfare. This paper constructs an incentive subsidy contract considering the decision‐making powers of the service level and calculating the proper frequency elasticity aiming at two problems of performance‐based contracts. Meanwhile, we consider a role of bus operators ignored by most researchers. Under the scheme, the decision‐making power of the service level is discussed based on five assumptions, and meanwhile, bus operators are motivated to reduce cost and improve service level in the scheme. The case of the bus service of Arao city indicates that the optimal frequency equals to zero when bus operators decide frequency. If bus operators determine efforts, the optimal effort also equals to zero with the goal of maximizing the profit. Also, bus operators can play their roles in lessening cost and improving service level to help bus operators and the local government achieve a win‐win situation, which maximizes the social benefit in this subsidy scheme when all factors are decided by the government. Copyright © 2013 John Wiley & Sons, Ltd.

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 score0.883
Threshold uncertainty score0.458

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 communication0.0000.001
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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