Application of Large Language Models in Intelligent Transportation Systems: A Systematic Review

Intelligent Transportation Systems (ITS) have revolutionized transportation engineering by enhancing safety, reducing congestion, and promoting sustainability. ITS operates by collecting real-time data using various sensors and performing decision-making using deep learning models. In recent times, Large Language Models (LLMs) have emerged as advanced deep learning models that are increasingly being adopted to solve complex problems, analyze data, and generate coherent human language. This paper presents a comprehensive review of the application of LLM in the ITS framework. The integration of LLMs has demonstrated significant advancements in autonomous driving, traffic engineering, and smart cities. By summarizing the latest research findings, this review paper highlights the potential contribution of LLM to safer and sustainable transportation. The findings of this article not only provide insights into the current application of LLM but also highlight the instrumental role of LLM in future ITS solutions, ensuring long-term benefits of the solutions that can adapt to future demands.