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Record W2588780433 · doi:10.5539/ijsp.v6n2p9

Evaluating the Influence of Taxi Subsidy Programs on Mitigating Difficulty Getting a Taxi in Basis of Taxi Empty-loaded Rate

2017· article· en· W2588780433 on OpenAlexvenueno aff
Jialin Wen, Min Zou, Yikai Ma, Hao Luo

Bibliographic record

VenueInternational Journal of Statistics and Probability · 2017
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsSubsidyService (business)Mobile internetComputer scienceThe InternetBusinessTransport engineeringMarketingEngineeringEconomicsWorld Wide Web

Abstract

fetched live from OpenAlex

With the advent of the “Internet plus” era, a number of companies have established the service platform of taxi-hailing apps relying on the mobile Internet, which builds up a communication bridge between passengers and taxi drivers. Besides, taxi companies have initiated many subsidy programs. Based on the prediction model of passenger waiting time built in this paper, it has been proved thatthere exists a negative correlation between passenger waiting time and taxi empty-loaded rate. This paper also analyzes the influencing factors of taxi empty-loaded rate. The results show that the higher the taxi sharing rate is, the lower the taxi empty-loaded rate is. And the longer the average operation time is, the higher the taxi empty-loaded rate is. By comparing various taxi subsidy programs, this paper finally draws a conclusion that it will be much more difficult to take a taxi if taxi companies provide subsidies for passengers. But the difficulty in taking a taxi can be alleviated if taxi companies provide subsidies for taxi drivers.

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)0.002
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 score0.470
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.042
GPT teacher head0.332
Teacher spread0.290 · 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
Published2017
Admission routes1
Has abstractyes

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