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Keywords

Large Language Models (LLMs), Digital technologies, Mobility, Multi-Criteria Decision-Making (MCDM), Triangular Neutrosophic Sets (TriNSs)

Article Type

Original Article

Abstract

The swift development of digital technologies, especially Large Language Models (LLMs), offers a revolutionary opportunity to improve urban mobility. LLMs are used widely in various areas, like traffic prediction, autonomous vehicle (AV) communication, and customized trip assistance. General speaking, users now consider the integration of LLMs in mobility to be intelligent friends. The study’s primary objective is to demonstrate how an integrated LLM in mobility services can significantly increase network efficiency, lessen congestion, and improve each traveler’s experience. Practically speaking, deploying a suitable LLM for providing various services to users is imperative. Additionally, this process is an endeavor due to uncertainty and contradictory criteria. To provide a solid technique for evaluating LLMs in mobility applications, this study attempts to suggest a novel evaluation framework that combines uncertainty theory and Multi-Criteria Decision-Making (MCDM). Hence, entropy and weighted sum model (WSM) of MCDM techniques with Triangular Neutrosophic Sets (TriNSs) of uncertainty theory to support the expert panel in vague situations.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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