  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.
