Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions

TL;DR

This paper reviews how Large Language Models can enhance Intelligent Transportation Systems by improving traffic management, safety, and autonomous driving, while discussing challenges and future research directions.

Contribution

It provides a comprehensive overview of LLM applications in ITS, highlighting recent advances, potential benefits, and addressing key challenges for future development.

Findings

LLMs can improve traffic flow prediction and vehicle detection.

Integration of LLMs enhances autonomous driving capabilities.

Identifies challenges like data scarcity and ethical issues in ITS applications.

Abstract

Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This paper comprehensively reviews the transformative potential of Large Language Models (LLMs) in optimizing ITS. Initially, we provide an extensive overview of ITS, highlighting its components, operational principles, and overall effectiveness. We then delve into the theoretical background of various LLM techniques, such as GPT, T5, CTRL, and BERT, elucidating their relevance to ITS applications. Following this, we examine the wide-ranging applications of LLMs within ITS, including traffic flow prediction, vehicle detection and classification, autonomous driving, traffic sign recognition, and pedestrian detection. Our analysis reveals how these advanced models can significantly enhance traffic management and safety. Finally, we explore the challenges and limitations LLMs face in ITS, such as data availability, computational constraints, and ethical considerations. We also present several future research directions and potential innovations to address these challenges. This paper aims to guide researchers and practitioners through the complexities and opportunities of integrating LLMs in ITS, offering a roadmap to create more efficient, sustainable, and responsive next-generation transportation systems.