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Record W2082715400 · doi:10.1287/trsc.1070.0206

Passenger Flow Model for Airline Networks

2008· article· en· W2082715400 on OpenAlex
Jonathan Dumas, François Soumis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTransportation Science · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsPolytechnique MontréalGroup for Research in Decision Analysis
Fundersnot available
KeywordsFlow networkFlow (mathematics)Variable (mathematics)Distribution (mathematics)Air travelTravel timeComputer scienceTransport engineeringOperations researchAviationEconometricsEngineeringEconomicsMathematicsMathematical optimizationAerospace engineering

Abstract

fetched live from OpenAlex

We present a model that rapidly finds an approximation of the expected passenger flow on an airline network, given forecast data concerning (1) the distribution of the demand for each itinerary, seen as a random variable; (2) the time distribution of booking requests for each itinerary; and (3) the proportion of spill (from an itinerary) that is attracted to a given alternative itinerary. Solutions are found in a few seconds for a 30,000 itinerary network. Results differ from the expected passenger flow found by a simulation by about 0.1% for load factors below 80%.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.091
GPT teacher head0.257
Teacher spread0.167 · 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