Prospects for Using Large Language Models of Artificial Intelligence in Transport

Andrey A. Tyugashev

doi:10.20295/2413-2527-2026-145-33-40

UDC

004.8

VAK Russia

1.2.1 1.2.2

PROSPECTS FOR USING LARGE LANGUAGE MODELS OF ARTIFICIAL INTELLIGENCE IN TRANSPORT Prospects for Using Large Language Models of Artificial Intelligence in Transport

АА Аа

Published in Intellectual Technologies on Transport · Pages 33–40 · Rubric: ARTIFICIAL INTELLIGENCE AND TRANSPORT SYSTEMS

DOI: https://doi.org/10.20295/2413-2527-2026-145-33-40 · EDN: KTNBAU

Received: 22.01.2026 Accepted: 06.02.2026 Published: 25.03.2026

Authors

Andrey A. Tyugashev

ORCID Scopus Scholar SPIN

Andrey A. Tyugashev

Academic degree: doctor of technical sciences

Science rang: docent

Automation and Information Technology Institute, Samara State Technical University Professor · Computer Science and Computer Engineering Department · Russian Federation · Samara

ORCID Scopus Scholar SPIN

ABSTRACT

This paper explores the potential of using large language models (LLMs), such as GPT-5.2 and Gemini 3, in the transportation industry through applications in vehicle design, autonomous navigation, traffic control, and other areas. Special attention is given to augmented sampling generation and multimodal processing. Key issues discussed include safety certification, model transparency, and ethical considerations of their implementation. Purpose: to investigate the prospects for using AI agents based on LLMs in the transport industry. Results: this paper considers the use of augmented sampling generation and multimodal data processing, along with examples, including traffic light control using AI, simulation scenario generation and driver fatigue analysis. Theoretical Significance: this paper concludes that the synergy between AI and transportation will inevitably lead to increased safety and efficiency, and that LLMs will play a significant role in future intelligent adaptive transport systems.

KEYWORDS

large language models, transportation, AI agents, predictive maintenance, generative design, safety-critical systems

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GOST

Tyugashev A. A. Prospects for Using Large Language Models of Artificial Intelligence in Transport // . 2026. pp. 33-40. DOI: https://doi.org/10.20295/2413-2527-2026-145-33-40. EDN: KTNBAU. (Date of access 19.08.2026).

MLA

Andrey A. Tyugashev. "Prospects for Using Large Language Models of Artificial Intelligence in Transport." (2026): 33-40. DOI: https://doi.org/10.20295/2413-2527-2026-145-33-40. EDN: KTNBAU.

APA

Tyugashev A. A. (2026). Prospects for Using Large Language Models of Artificial Intelligence in Transport. , 33-40. https://doi.org/10.20295/2413-2527-2026-145-33-40 EDN: KTNBAU.

Chicago

Andrey A. Tyugashev. "Prospects for Using Large Language Models of Artificial Intelligence in Transport." , no. (2026): 33-40. https://doi.org/10.20295/2413-2527-2026-145-33-40. EDN: KTNBAU.

WIKI

{{cite journal | author = Tyugashev A. A. | title = Prospects for Using Large Language Models of Artificial Intelligence in Transport | journal = | year = 2026 | pages = 33-40 | doi = 10.20295/2413-2527-2026-145-33-40 }} EDN: KTNBAU

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PROSPECTS FOR USING LARGE LANGUAGE MODELS OF ARTIFICIAL INTELLIGENCE IN TRANSPORT Prospects for Using Large Language Models of Artificial Intelligence in Transport

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