Generative models for the evolution of transportation systems

The rapid acceleration of big data and artificial intelligence has spurred the application of advanced machine learning methods to address multifaceted transportation challenges. Among these, generative models (GMs) have garnered significant attention, demonstrating great potential for advancing intelligent transportation systems. This paper provides a comprehensive investigation into the applications and potential of GMs within this domain. First, the paper systematically reviews the fundamental principles, architectures, and comparative characteristics of mainstream generative models. The primary contribution is an in-depth review of GM applications across three core areas: trajectory generation, traffic flow prediction, and autonomous driving. In trajectory generation, we examine how GMs synthesize realistic data to address data scarcity and privacy preservation. For traffic flow, the review covers GM-based approaches for prediction and critical data imputation tasks. In autonomous driving, the analysis details GM applications in sensor data restoration, perception enhancement, realistic scenario simulation, and behavior prediction. Although GMs have shown significant value, their full potential remains underexplored. Therefore, this paper identifies and discusses promising avenues for future research, including the integration of diffusion models in autonomous driving, the use of GMs for infrastructure planning, and their application in enhancing traffic safety. This paper is anticipated to serve as a comprehensive reference for researchers exploring generative models in transportation.