Large Language Models (LLMS) as Intelligent Enablers for Transportation Research, Planning, and System Optimization: A Comprehensive Review and Future Perspective

Abstract

The rapid evolution of Artificial Intelligence (AI) has transformed the landscape of transportation research, with Large Language Models (LLMs) emerging as a key technological enabler for intelligent data analysis, decision-making, and system optimization. LLMs, such as GPT, BERT, and PaLM, have demonstrated remarkable natural language understanding and reasoning capabilities that are now being integrated into transportation systems for predictive modeling, urban mobility analysis, traffic forecasting, and policy planning. This paper provides a comprehensive overview of the role of LLMs in transportation research, emphasizing their applications, benefits, challenges, and future potential. It explores how these models can enhance traffic management, autonomous vehicle communication, logistics optimization, and sustainability studies. Furthermore, it identifies the limitations of LLMs, including data bias, interpretability, and ethical considerations, while presenting a forward-looking perspective on integrating LLMs with emerging transportation technologies such as the Internet of Things (IoT), digital twins, and smart infrastructure systems.