Advancing ITS Applications with LLMs: A Survey on Traffic Management, Transportation Safety, and Autonomous Driving

Dingkai Zhang, Huanran Zheng, Wenjing Yue, Xiaoling Wang

Abstract: In the past two years, large language models (LLMs) have shown extensive attention in the applications of intelligent transportation systems (ITS). Despite the huge potential, there is still a lack of comprehensive understanding of the advantages, challenges, and future efforts of LLMs in the transportation field. In this paper, we present a systematic investigation in this field, underlining their approaches and performance in improving forecasting accuracy, decision-making capability, and sim-to-real tasks. We first explore the current applications of LLMs in traffic management, transportation safety, and autonomous driving, as well as analyze their advantages and limitations. Then we also list some typical datasets employed within this domain. Challenges and prospects of the development of LLMs for ITS applications are discussed, encompassing technological, security, and policy aspects. We aim to offer a holistic overview of the transformative impact of LLMs in the transportation field, highlight their significance, and provide some possible views for future research and development.