Diffusion Models for Intelligent Transportation Systems: A Survey

Mingxing Peng, Kehua Chen, Xusen Guo, Qiming Zhang, Hui Zhong, Meixin Zhu, Hai Yang

Abstract: Intelligent Transportation Systems (ITS) play a crucial role in enhancing traffic efficiency and safety. Recently, diffusion models have emerged as transformative tools for addressing the complex challenges faced within ITS, including traffic uncertainty, data multimodality, and data imperfections. This paper presents a comprehensive survey of diffusion models in ITS, exploring both theoretical and practical dimensions. We begin by introducing the theoretical foundations of diffusion models and their key variants, such as conditional and latent diffusion models, highlighting their probabilistic modeling nature, capacity to model complex multimodal traffic data, and support for controllable generation. Next, we analyze the major challenges in ITS and explain how diffusion models offer robust, flexible, and controllable solutions, thereby elucidating their unique advantages in this domain. We then conduct a multi-perspective examination of current applications of diffusion models across ITS domains, including autonomous driving, traffic simulation, traffic forecasting, and traffic safety. Finally, we discuss state-of-the-art diffusion model techniques and highlight key research directions within ITS that merit further exploration. Through this structured overview, we aim to equip researchers with a comprehensive understanding of diffusion models in ITS, thereby fostering their future applications in the transportation domain. An open-source repository accompanying this survey is available at: https://github.com/Pemixing/Diffusion-Models-in-ITS-A-Survey

External IDs:dblp:journals/tits/PengCGZZZY25