This research provides the first comprehensive review of Large Language Models (LLMs) applications in transportation systems, synthesizing methodologies and identifying future opportunities.

• Rapid advancement period (2023-2025) marked significant growth in LLM adoption for transportation applications
• Systematically analyzes LLM implementation across traffic forecasting, autonomous driving, and transportation planning
• Identifies critical gaps in current research while outlining methodological frameworks for implementation
• Proposes future research directions to maximize LLM potential in transportation engineering

For engineering professionals, this research offers valuable frameworks to integrate cutting-edge AI capabilities into transportation systems, potentially improving efficiency, safety, and sustainability of mobility infrastructure.

Large Language Models for Traffic and Transportation Research: Methodologies, State of the Art, and Future Opportunities