Generative artificial intelligence in intelligent transportation systems: A systematic review of applications

作者：Rui Rong, Shoufeng Ma, Nianlu Ren, Qinping Lin, Ning Jia · 发表于：Frontiers of Engineering Management · 年份：2025 · DOI：10.1007/s42524-025-4241-9 · 被引用次数：22 · 研究领域：Traffic Prediction and Management Techniques、Autonomous Vehicle Technology and Safety、Traffic control and management

Abstract Rapid urbanization is reshaping mobility demands, calling for advanced intelligence and management capabilities in urban transport systems. Generative Artificial Intelligence (AI) presents new opportunities to enhance the efficiency and responsiveness of Intelligent Transportation Systems (ITS). This paper reviews the existing literature in transportation and AI to investigate the core technologies of Artificial Intelligence Generated Content (AIGC)–including dialog and reasoning, prediction and decision making, and multimodal generation. Applications are summarized across the four primary ITS subsystems (road subsystem, vehicle subsystem, traveler subsystem and management subsystem). This paper finds that AIGC has become an important way to promote the progress and development of ITS by exploring the research progress of cutting-edge technologies such as data generation, assisted driving decision-making, and intelligent traffic prediction. Meanwhile, this paper explores the potential challenges that AIGC brings to human society from the perspectives of safety risks of fake content, human-machine relationships, social cognition and emotional trust, and related ethical issues, providing insights for the development of safer and more sustainable ITS in the future.