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Record W4296211873 · doi:10.1109/tits.2022.3202111

Autonomous Bus Operation Alternatives in Urban Areas Using Fuzzy Dombi-Bonferroni Operator Based Decision Making Model

2022· article· en· W4296211873 on OpenAlexaff
Muhammet Deveci, Dragan Pamučar, Ilgın Gökaşar, Witold Pedrycz, Xin Wen

Bibliographic record

VenueIEEE Transactions on Intelligent Transportation Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsUniversity of Alberta
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsFuzzy logicOperator (biology)Autonomous system (mathematics)Computer scienceTransport engineeringBonferroni correctionOperations researchEngineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Advances in V2V, V2I, and autonomous vehicle technologies have made autonomous buses possible in cities. Soon, autonomous bus operations will be common in urban areas, which will improve sustainability, safety, and the city’s technology. These buses have different operation types. Each operation has advantages and disadvantages. Therefore, the goal of this study is to serve as a guide for decision-makers during the transition to autonomous buses. Four alternatives are evaluated based on eleven criteria organized under four main aspects, namely autonomous buses for special uses, autonomous buses for last-mile uses, autonomous cars in mixed traffic, and autonomous buses in closed systems. We propose an Ordinal Priority Approach (OPA) method for determining the criteria weights and application of fuzzy Dombi Bonferroni (DOBI) methodology for the evaluation of alternatives. When compared to the other three alternatives in this study, the results show that deploying autonomous buses in mixed traffic is the most advantageous option.

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 categoriesMeta-epidemiology (narrow)
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 score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.276
Teacher spread0.243 · 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.

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

Citations21
Published2022
Admission routes1
Has abstractyes

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