Cross-city transfer learning: Applications and challenges for smart cities and sustainable transportation

作者：Ying Yang, Jiahao Zhan, Yang Liu, Qi Wang · 发表于：Communications in Transportation Research · 年份：2025 · DOI：10.1016/j.commtr.2025.100206 · 被引用次数：27 · 研究领域：Human Mobility and Location-Based Analysis、Smart Cities and Technologies、Traffic Prediction and Management Techniques

Cross-city transfer learning (CCTL) has emerged as a crucial approach for managing the growing complexity of urban data and addressing the challenges posed by rapid urbanization. This paper provides a comprehensive review of recent advances in CCTL, with a focus on its applications in urban computing tasks, including prediction, detection, and deployment. We examine the role of CCTL in facilitating policy adaptation and influencing behavioral change. Specifically, we provide a systematic overview of widely used datasets, including traffic sensor data, GPS trajectory data, online social network data, and map data. Furthermore, we conduct an in-depth analysis of methods and evaluation metrics employed across different CCTL-based urban computing tasks. Finally, we emphasize the potential of cross-city policy transfer in promoting low-carbon and sustainable urban development. This review aims to serve as a reference for future urban development research and promote the practical implementation of CCTLs.