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A Method for Determining Length of Freeway Work Zone Based on Classification of Service Level

2013· article· en· W2129161092 on OpenAlexaff
Ke Man Wu, Lian De Zhong

Bibliographic record

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsVisSimTraffic flow (computer networking)Level of serviceWork (physics)Traffic volumeTransport engineeringService (business)SoftwareComputer scienceTraffic simulationWork zoneVolume (thermodynamics)SimulationEngineeringComputer networkMicrosimulationBusiness

Abstract

fetched live from OpenAlex

based on the relationship between speed and density according to the classification criteria of freeway service level when the design speed is 80km/h in Highway capacity manual, the method of microscopic traffic flow simulation supported by VISSIM software is adopted, such that, in the situation the capacity and desired speed are guaranteed to be consistent with the reality, the proposed values of the length of work zone under different traffic composition and traffic volume are given in the condition that a 2-way-8-lane freeway is half closed for construction with 2-way-4-lane being left open for traffic.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.206
GPT teacher head0.427
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations3
Published2013
Admission routes1
Has abstractyes

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