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Record W2810178555 · doi:10.5539/mas.v12n7p144

Fuzzy Comprehensive Evaluation Model for Road Capacity of Open Neighborhood -- Based on Improved NS Cellular Automata

2018· article· en· W2810178555 on OpenAlexvenueno aff
Xiao-Yan Cao, Bingqian Liu, Jian Cao, Yuanbiao Zhang

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCellular automatonObstacleComputer scienceFuzzy logicTransport engineeringEvaluation methodsArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

With the rapid development of motorization in our country, gated communities have become the obstacle of the development of urban traffic network. In this paper, focusing on the impact of gated communities on the traffic situation in surrounding area before and after it is opened, we firstly establish a comprehensive evaluation system by fuzzy comprehensive evaluation method and then build up the improved NS model based on Cellular Automata, which put forward the concept of internal road sharing rate in communities. Finally, we select the urban district of different types and surrounding roads in Shanghai as a case, and through simulation and evaluation, we found that the opening of communities is feasible. What’s more, compared with gated communities, the two-way 2-lane urban opening communities have the most optimal effect on improving road capacity.

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)0.001
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: none
Teacher disagreement score0.865
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.056
GPT teacher head0.297
Teacher spread0.241 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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