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  5. Accelerating the integration of the metaverse into urban transportation using fuzzy trigonometric based decision making

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Article
en
2023

Accelerating the integration of the metaverse into urban transportation using fuzzy trigonometric based decision making

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en
2023
Vol 127
Vol. 127
DOI: 10.1016/j.engappai.2023.107242

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Witold Pedrycz
Witold Pedrycz

University of Alberta

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Muhammet Deveci
Dragan Pamučar
Ilgın Gökaşar
+3 more

Abstract

Metaverse is defined as a fictional universe that could serve as a simulation environment of reality. Beginning in the past with games, it becomes increasingly integrated into human life as time passes. Metaverse usage is inevitable in every aspect of life. One of its potential application areas could be urban transportation. A novel fuzzy trigonometric based on the combination of the Full Consistency Method (FUCOM) and Combined Compromise Solution (CoCoSo) is proposed to rank three alternatives with twelve criteria under four major aspects: managerial, safety, user, and urban mobility. In the first stage, fuzzy FUCOM methods are used to calculate the weights of the criteria. In the second stage, the fuzzy trigonometric based CoCoSo method is applied to evaluate and rank the alternatives. The proposed model enables the nonlinear processing of complex and uncertain information using fuzzy trigonometric functions. The findings demonstrate focusing on a particular age group can make it easier to integrate the metaverse with urban transportation. The findings of this study have the potential to serve as a guide for decision-makers. The metaverse-based applications could be started by policymakers, which is a promising opportunity with potential boundaries beyond human comprehension making this statement weaker.

How to cite this publication

Muhammet Deveci, Dragan Pamučar, Ilgın Gökaşar, Luis Martı́nez, Mario Köppen, Witold Pedrycz (2023). Accelerating the integration of the metaverse into urban transportation using fuzzy trigonometric based decision making. , 127, DOI: https://doi.org/10.1016/j.engappai.2023.107242.

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Publication Details

Type

Article

Year

2023

Authors

6

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1016/j.engappai.2023.107242

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