Intelligent transportation systems play a crucial role in modern traffic management and optimization, greatly improving traffic efficiency and safety. With the rapid development of generative artificial intelligence (Generative AI) technologies in the fields of image generation and natural language processing, generative AI has also played a crucial role in addressing key issues in intelligent transportation systems, such as data sparsity, difficulty in observing abnormal scenarios, and in modeling data uncertainty. In this review, we systematically investigate the relevant literature on generative AI techniques in addressing key issues in different types of tasks in intelligent transportation systems. First, we introduce the principles of different generative AI techniques, and their potential applications. Then, we classify tasks in intelligent transportation systems into four types: traffic perception, traffic prediction, traffic simulation, and traffic decision-making. We systematically illustrate how generative AI techniques addresses key issues in these four different types of tasks. Finally, we summarize the challenges faced in applying generative AI to intelligent transportation systems, and discuss future research directions based on different application scenarios.

翻译：智能交通系统在现代交通管理与优化中发挥着关键作用，显著提升了交通效率与安全性。随着生成式人工智能（Generative AI）技术在图像生成和自然语言处理领域的快速发展，生成式人工智能在解决智能交通系统中的关键问题（如数据稀疏性、异常场景观测困难以及数据不确定性建模）方面也发挥了重要作用。本综述系统性地研究了生成式人工智能技术在应对智能交通系统中不同类型任务的关键问题时的相关文献。首先，我们介绍了不同生成式人工智能技术的原理及其潜在应用。随后，我们将智能交通系统中的任务划分为四类：交通感知、交通预测、交通仿真和交通决策。我们系统地阐述了生成式人工智能技术如何应对这四类任务中的关键问题。最后，我们总结了将生成式人工智能应用于智能交通系统时所面临的挑战，并基于不同应用场景探讨了未来的研究方向。