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Record W2903599528 · doi:10.1109/itsc.2018.8569788

Modelling Hourly Vehicle Flows by a Finite Mixture of Simple Circular Normal Distributions

2018· article· en· W2903599528 on OpenAlexaff
Pavel Krömer, Martin Hasal, Jana Nowaková, Jana Heckenbergerová, Petr Musı́lek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRepresentation (politics)Simple (philosophy)Computer scienceFinite element methodTraffic flow (computer networking)Differential evolutionFlow (mathematics)Differential (mechanical device)Work (physics)Statistical modelAlgorithmMathematical optimizationMathematicsData miningEngineeringArtificial intelligenceGeometryAerospace engineering

Abstract

fetched live from OpenAlex

Accurate modelling and representation of traffic flows is an important element of intelligent transportation systems, urban planning, and smart environments in general. In this work, location-specific hourly traffic flows are represented by finite mixtures of circular normal statistical distributions. The parameters of the finite mixtures are found by differential evolution, an evolutionary algorithm that is able to fit the statistical models to data with a high level of accuracy. The results are represented by circular plots that can be used as a form of visually appealing and easily understandable fingerprints of the underlying traffic flow patterns.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.324

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 head0.008
GPT teacher head0.197
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations4
Published2018
Admission routes1
Has abstractyes

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