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Record W4210507036 · doi:10.21203/rs.3.rs-1334110/v1

Short-time traffic flow prediction based on seasonal gray Fourier model

2022· preprint· en· W4210507036 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldDecision Sciences
TopicGrey System Theory Applications
Canadian institutionsnot available
Fundersnot available
KeywordsFourier transformFourier seriesGray (unit)Fourier analysisNonlinear systemTime seriesStatisticsMathematicsMeteorologyEconometricsEnvironmental scienceApplied mathematicsComputer scienceGeographyMathematical analysisPhysics

Abstract

fetched live from OpenAlex

Abstract To handle the periodic, nonlinear, and stochastic aspects of short-time traffic flow data, a seasonal gray Fourier model based on the complex Simpson formula is proposed. The seasonal GM (1, 1) model is used to optimize the background values first, and then the prediction results are adjusted using the Fourier series method. The new model was applied to the prediction of traffic flow on Whitemud Drive in Canada, and the numerical results indicated that the new model's mean absolute percentage error was 1.54 percent and its fit was 0.996, which were significantly better than those of the traditional GM (1, 1) model, the seasonal GM (1, 1) model, and the Fourier optimized seasonal GM (1, 1) model.

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)0.027
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0040.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.204
GPT teacher head0.462
Teacher spread0.259 · 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