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Record W1989695244 · doi:10.1139/l04-066

Optimizing geometric design of single-lane roundabouts: consistency analysis

2004· article· en· W1989695244 on OpenAlexvenueno aff
Said M. Easa, Atif Mehmood

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
Fundersnot available
KeywordsRoundaboutConsistency (knowledge bases)Geometric designPath (computing)Process (computing)RADIUSOptimal designInscribed figureComputer scienceMathematicsMathematical optimizationSimulationEngineeringGeometryTransport engineeringStatistics

Abstract

fetched live from OpenAlex

One of the main objectives to promote traffic safety at roundabouts is design consistency. Design consistency ensures that the speed differences along a vehicle path or between conflicting paths are smaller than a specified criterion. Traditionally, roundabout design involves an iterative process. Initially, the vehicle path radii are determined from drawing each path by freehand on the proposed roundabout geometry, and then the speeds along path elements are calculated. The process is repeated until design consistency is satisfied. This paper presents an optimization model that directly provides the design parameters that maximize design consistency. The radii of different vehicle paths (through, right, and left) are mathematically modeled for given design parameters. The optimum design parameters include vehicle path radii, approach entry widths, inscribed circle diameter, circulating width, and central island diameter. The optimization model is applicable to single-lane roundabouts with four legs intersecting at right angles. The proposed model not only makes the design process more efficient, but also guarantees the optimum design consistency.Key words: geometric design, roundabouts, horizontal curve radius, consistency, safety, optimization.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.011
GPT teacher head0.167
Teacher spread0.156 · 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

Citations18
Published2004
Admission routes1
Has abstractyes

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