# Diffusion Models for Intelligent Transportation Systems: A Survey

arXiv: https://arxiv.org/abs/2409.15816 · HTML: https://mlsanity.com/paper/2409.15816/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2024.

## Built on by (most cited first, 15 of 15)

- [Efficient Diffusion Models: A Survey](https://mlsanity.com/paper/2502.06805/) (2025)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios](https://mlsanity.com/paper/2505.11247/) (2025)
- [Generative AI in Transportation Planning: A Survey](https://mlsanity.com/paper/2503.07158/) (2025)
- [Safety-Critical Traffic Simulation with Guided Latent Diffusion Model](https://mlsanity.com/paper/2505.00515/) (2025)
- [nuPlan-R: A Closed-Loop Planning Benchmark for Autonomous Driving via Reactive Multi-Agent Simulation](https://mlsanity.com/paper/2511.10403/) (2025)
- [Diffusion^2: Dual Diffusion Model with Uncertainty-Aware Adaptive Noise for Momentary Trajectory Prediction](https://mlsanity.com/paper/2510.04365/) (2025)
- [VLM as Strategist: Adaptive Generation of Safety-critical Testing Scenarios via Guided Diffusion](https://mlsanity.com/paper/2512.02844/) (2025)
- [RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving](https://mlsanity.com/paper/2602.07339/) (2026)
- [Contrastive Learning-Driven Traffic Sign Perception: Multi-Modal Fusion of Text and Vision](https://mlsanity.com/paper/2507.23331/) (2025)
- [Collaborative-Distilled Diffusion Models (CDDM) for Accelerated and Lightweight Trajectory Prediction](https://mlsanity.com/paper/2510.00627/) (2025)
- [A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public Transport](https://mlsanity.com/paper/2510.24375/) (2025)
- [IRL-DAL: Safe and Adaptive Trajectory Planning for Autonomous Driving via Energy-Guided Diffusion Models](https://mlsanity.com/paper/2601.23266/) (2026)
- [Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models](https://mlsanity.com/paper/2605.24924/) (2026)
- [A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform](https://mlsanity.com/paper/2610.00926/) (2026)

## Abstract (arXiv)

Intelligent Transportation Systems (ITS) are vital in modern traffic management and optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models have emerged as transformative tools for addressing complex challenges within ITS. In this paper, we present a comprehensive survey of diffusion models for ITS, covering both theoretical and practical aspects. First, we introduce the theoretical foundations of diffusion models and their key variants, including conditional diffusion models and latent diffusion models, highlighting their suitability for modeling complex, multi-modal traffic data and enabling controllable generation. Second, we outline the primary challenges in ITS and the corresponding advantages of diffusion models, providing readers with a deeper understanding of the intersection between ITS and diffusion models. Third, we offer a multi-perspective investigation of current applications of diffusion models in ITS domains, including autonomous driving, traffic simulation, trajectory prediction, and traffic safety. Finally, we discuss state-of-the-art diffusion model techniques and highlight key ITS research directions that warrant further investigation. Through this structured overview, we aim to provide researchers with a comprehensive understanding of diffusion models for ITS, thereby advancing their future applications in the transportation domain.