This paper presents a comprehensive 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.

Big Data applications have transformed Intelligent Transport Systems (ITS), enabling improvements in traffic management, safety, and efficiency. This study presents a bibliometric analysis and review of the recent advancements of big data applications in ITS. For this bibliometric analysis, the Scopus database was utilized due to its extensive resources. Various tools, such as RStudio, VOSviewer, Excel, and Python, were used to analyze data and identify trends, patterns, and relationships in the selected articles through performance analysis and science mapping. The study examined 447 articles published between 2014 and 2023. The analysis indicates that research in this field has experienced exponential growth annually, although it experienced setbacks in 2020 due to the global pandemic before regaining momentum. While the number of research publications has risen sharply, the slower growth in citations highlights the need for a greater focus on producing higher-quality research. Our investigation revealed that the most significant research efforts focused on traffic flow prediction, traffic anomaly prediction, traffic safety, the integration of big data with the Internet of Things (IoT) and the Internet of Vehicles (IoV). China, United States, and Canada were the primary contributors to this field, with China conducting the majority of studies. We summarized and critically reviewed the most cited papers, as well as those that present the most significant innovations in this field. We found that ethical, privacy, and security concerns related to the use of Big Data in ITS have received limited attention. This work aims to serve as a valuable resource for researchers and practitioners, encouraging innovation and the development of more effective and sustainable transportation solutions.

The integration of machine learning (ML) into intelligent transport systems (ITS) has significantly enhanced the efficiency, security, and sustainability of urban transportation. Bibliometric analysis can identify existing research hotspots and the areas of interest and predict development trends, which can help to identify the current advancements in the role of ML in ITS. The Scopus database was used for the analysis because of its extensive collection of scholarly publications. This study examines a comprehensive set of 534 papers published between 2001 and 2023, utilizing various tools, including RStudio, VOSviewer, Excel, Publish & Perish, and Python to analyze data. The publication trend indicates that research in this area has grown exponentially each year. The keyword analysis indicated that key research interests within ML applications in ITS focus on network and communication technologies, urban mobility and infrastructure, safety and efficiency enhancements, and emerging topics such as demand-responsive transport and integrated decision-making. Europe leads in publication count, while East Asia, particularly China, contributes significantly, and research from regions such as North America, the Middle East, and Oceania, despite lower output, shows higher citation averages. This study also provides a comprehensive review of the top 50 most-cited publications, identifying six key focus areas and demonstrating the significant advancements in this field. However, this study is limited by keyword and tool biases, potential scope oversight, and reliance on a single database. Future research should expand keywords, use diverse tools, and include more databases. Overall, this study aims to serve as a valuable resource for researchers and practitioners, fostering innovation and developing more effective and sustainable transportation solutions.