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Record W2913520632 · doi:10.1139/cjce-2018-0381

Protected–permissive left turn phasing with flashing yellow arrow signal: study of red intervals for an effective phase transition

2019· article· en· W2913520632 on OpenAlexvenueno aff
Muqtasid Mahbub, Min-Wook Kang, Joyoung Lee

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
FundersAlabama Department of TransportationFederal Highway AdministrationUniversity of South Alabama
KeywordsPhaserFlashingPermissiveIntersection (aeronautics)Interval (graph theory)Turn (biochemistry)ArrowSIGNAL (programming language)SimulationMathematicsControl theory (sociology)Computer scienceEngineeringTransport engineeringPhysicsElectrical engineeringBiologyCombinatoricsArtificial intelligence

Abstract

fetched live from OpenAlex

Protected–permissive left turn (PPLT) phasing has been popular and widely used in many urban intersections in North America because of its operational benefits. A significant number of intersections have recently been upgraded with four-section signal heads with flashing yellow arrow (FYA) indication for an effective protected–permissive left turn operation. The present study seeks to find appropriate length of two red intervals whose roles are important, but different during the transition period of FYA-PPLT phasing. One is a red interval for delayed-start of permissive left turn movements; the other is additional red interval for delayed-start of opposing through movements. Micro-traffic simulation and conflict analysis are explored to assess the effects of the red intervals on intersection efficiency and safety. A useful reference, which describes the balanced length of the two red intervals under varying traffic levels, is developed as a result.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.200
Teacher spread0.193 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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