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Record W1989450531 · doi:10.1139/l05-078

Optimizing geometric design of roundabouts: multi-objective analysis

2006· article· en· W1989450531 on OpenAlexfundvenueno aff
Atif Mehmood, Said M. Easa

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRoundaboutGeometric designConsistency (knowledge bases)Computer scienceIterative methodKey (lock)Process (computing)Optimal designFunction (biology)Mathematical optimizationTransport engineeringSimulationEngineeringAlgorithmMathematics

Abstract

fetched live from OpenAlex

The main objectives of roundabout design are to maximize traffic safety and operational efficiency. Traditionally, because of the complexity of the system and the multiple objectives involved, the design process is iterative and time-consuming. A minor change in the geometry can result in significant changes in the system performance (operation and safety). This paper presents an optimization model that directly provides the roundabout geometry that optimizes two objectives: design consistency and operational efficiency. Design consistency is represented by the mean difference in operating speeds for various conflicting vehicle paths and operational efficiency is represented by the average roundabout delay. Vehicle paths (through, right, and left) and roundabout delay are modeled for all roundabout approaches. The input geometric data to the model can be easily obtained from an aerial photograph of the selected site using a geographic information systems (GIS) software. The system performance is optimized subject to geometric and traffic constraints. The proposed model is applicable to single-lane roundabouts (urban and rural) with four legs intersecting at right angles. Application of the model to an actual proposed roundabout site is presented. This proposed approach provides the optimum solution directly and is also more efficient than the traditional iterative approach. Key words: geometric design, roundabouts, horizontal curve, radius, optimization, consistency, capacity, traffic delay.

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 score0.983
Threshold uncertainty score0.970

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 head0.008
GPT teacher head0.171
Teacher spread0.163 · 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

Citations19
Published2006
Admission routes2
Has abstractyes

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