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Record W3163172691 · doi:10.1177/03611981211011478

Multi-Objective Evaluation Model of Single-Lane Roundabouts

2021· article· en· W3163172691 on OpenAlexaff
Hend Ahmed, Said M. Easa

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2021
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRoundaboutGeometric designOptimal designSustainabilityPoison controlComputer scienceTransport engineeringMathematical optimizationEngineeringMathematics

Abstract

fetched live from OpenAlex

Mobility, safety, and environmental sustainability are priorities in the geometric design of roundabouts. This paper presents a multi-objective optimization model that determines the geometric design elements of single-lane roundabouts using all three objectives. The user can specify weights for the objectives, or the model can determine the optimal weights. Mobility is defined in terms of roundabout delay and modeled using the United Kingdom empirical model. Safety is modeled in terms of collision frequency based on the methodology of the Highway Safety Manual. Environmental sustainability is represented by vehicle emissions (nitrogen oxides, hydrocarbons, carbon dioxide, and carbon monoxide) and is modeled using the vehicle-specific power methodology. The proposed model directly identifies the optimal geometric dimensions (decision variables) of the roundabout, including entry width, exit width, approach half-width, circulatory width, effective flare length, entry radius, entry angle, and inscribed circle diameter. The input data to the model include traffic data, site conditions, and limitations based on design guidelines. Application of the proposed model is illustrated using two actual roundabouts. The comparison results show that the proposed model provides substantial improvements in safety, mobility, and environmental sustainability compared with existing conditions. In addition, the model requires much less effort to apply compared with the traditional iterative method, and as such should be of interest to highway designers.

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)0.004
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: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.155
GPT teacher head0.386
Teacher spread0.231 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicVehicle emissions and performanceFrench-language works237,207