Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research
This paper presents a comprehensive review and bibliometric analysis of Large Language Models (LLMs) in transportation, exploring emerging trends, challenges and future research. Understanding their evolution and impact in transportation research is essential. The study used Scopus as the primary da...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11080381/ |
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| author | Mahbub Hassan Md. Emtiaz Kabir Muzammil Jusoh Hong Ki An Michael Negnevitsky Chengjiang Li |
| author_facet | Mahbub Hassan Md. Emtiaz Kabir Muzammil Jusoh Hong Ki An Michael Negnevitsky Chengjiang Li |
| author_sort | Mahbub Hassan |
| collection | DOAJ |
| description | This paper presents a comprehensive review and bibliometric analysis of Large Language Models (LLMs) in transportation, exploring emerging trends, challenges and future research. Understanding their evolution and impact in transportation research is essential. The study used Scopus as the primary data source, applying Bibliometrix, VOSviewer, and Python for performance analysis and science mapping. This study analyzes 161 peer-reviewed articles and reveals a 25.74% annual growth in scholarly output. IEEE Transactions on Intelligent Transportation Systems and IEEE Transactions on Intelligent Vehicles emerge as the most influential journals by publication volume and impact on LLM research. The findings highlight global disparities in research contributions, with China and the United States dominating by publication volume, followed by Germany and Canada, while developing regions exhibit lower scientific productivity. In addition, the study provides qualitative insights by reviewing recent LLM applications in transportation, examining their key contributions, methodological approaches, inherent limitations, and domain-specific challenges. Key research themes focus on autonomous mobility, traffic optimization, and sustainable transportation networks. Despite significant progress, several challenges remain, including decision-making uncertainties, computational scalability, and high energy consumption. Overcoming these challenges requires greater transparency through causal learning, enhanced reasoning via hybrid AI models, and inclusive frameworks that address algorithmic bias and ensure equitable adoption. |
| format | Article |
| id | doaj-art-b6e7a089ed8042b19a142d3bd061dbb2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b6e7a089ed8042b19a142d3bd061dbb22025-08-20T03:58:35ZengIEEEIEEE Access2169-35362025-01-011313254713259810.1109/ACCESS.2025.358931911080381Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future ResearchMahbub Hassan0https://orcid.org/0009-0006-1956-8159Md. Emtiaz Kabir1https://orcid.org/0009-0008-5534-9396Muzammil Jusoh2https://orcid.org/0000-0003-3870-6773Hong Ki An3https://orcid.org/0000-0003-4331-8281Michael Negnevitsky4https://orcid.org/0000-0002-5130-419XChengjiang Li5https://orcid.org/0000-0002-1864-2023Faculty of Civil Engineering and Technology, Universiti Malaysia Perlis (UniMAP), Arau, MalaysiaDepartment of Civil Engineering, Atish Dipankar University of Science and Technology (ADUST), Dhaka, BangladeshCentre of Excellence for Advanced Computing (AdvComp), Faculty of Electronic Engineering Technology, UniMAP, Arau, MalaysiaFaculty of Civil Engineering and Technology, Universiti Malaysia Perlis (UniMAP), Arau, MalaysiaSchool of Engineering, University of Tasmania, Hobart, AustraliaSchool of Management, Guizhou University, Guiyang, ChinaThis paper presents a comprehensive review and bibliometric analysis of Large Language Models (LLMs) in transportation, exploring emerging trends, challenges and future research. Understanding their evolution and impact in transportation research is essential. The study used Scopus as the primary data source, applying Bibliometrix, VOSviewer, and Python for performance analysis and science mapping. This study analyzes 161 peer-reviewed articles and reveals a 25.74% annual growth in scholarly output. IEEE Transactions on Intelligent Transportation Systems and IEEE Transactions on Intelligent Vehicles emerge as the most influential journals by publication volume and impact on LLM research. The findings highlight global disparities in research contributions, with China and the United States dominating by publication volume, followed by Germany and Canada, while developing regions exhibit lower scientific productivity. In addition, the study provides qualitative insights by reviewing recent LLM applications in transportation, examining their key contributions, methodological approaches, inherent limitations, and domain-specific challenges. Key research themes focus on autonomous mobility, traffic optimization, and sustainable transportation networks. Despite significant progress, several challenges remain, including decision-making uncertainties, computational scalability, and high energy consumption. Overcoming these challenges requires greater transparency through causal learning, enhanced reasoning via hybrid AI models, and inclusive frameworks that address algorithmic bias and ensure equitable adoption.https://ieeexplore.ieee.org/document/11080381/Artificial intelligencebibliometric analysisintelligent transportation systemslarge language modelssmart mobilitytransportation |
| spellingShingle | Mahbub Hassan Md. Emtiaz Kabir Muzammil Jusoh Hong Ki An Michael Negnevitsky Chengjiang Li Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research IEEE Access Artificial intelligence bibliometric analysis intelligent transportation systems large language models smart mobility transportation |
| title | Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research |
| title_full | Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research |
| title_fullStr | Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research |
| title_full_unstemmed | Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research |
| title_short | Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research |
| title_sort | large language models in transportation a comprehensive bibliometric analysis of emerging trends challenges and future research |
| topic | Artificial intelligence bibliometric analysis intelligent transportation systems large language models smart mobility transportation |
| url | https://ieeexplore.ieee.org/document/11080381/ |
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