大语言模型正重塑智能交通系统，提升管理效率与安全。
Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions
- 将GPT、BERT等大模型融入交通系统，实现语义理解与决策优化
- 在车流预测、自动驾驶等场景中显著提升识别与响应能力
- 适合交通算法研究者与智慧城市从业者参考
智能交通系统（ITS）是智慧城市建设的关键，关乎效率、生产率与环境可持续性。本文全面综述大型语言模型（LLMs）在优化ITS中的变革潜力。首先系统介绍ITS的构成、运行机制与实际效能；随后剖析GPT、T5、CTRL、BERT等典型LLM技术的理论基础及其在交通领域的适用性；接着探讨其在车流预测、车辆检测分类、自动驾驶、交通标志识别及行人检测等多场景的应用进展；分析表明，这些模型可显著增强交通管理与安全保障能力。最后，讨论了数据获取、计算资源、伦理问题等关键挑战，并提出未来研究方向与创新路径。本文旨在为研究人员与实践者提供融合LLMs于ITS的行动指南，助力构建更高效、可持续、敏捷的下一代交通系统。
原文摘要 · Abstract (English)
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.
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