@article{Yang2025, 
author = {Ying Yang and Jiahao Zhan and Yang Liu and Qi Wang},
title = {Cross-city transfer learning: Applications and challenges for smart cities and sustainable transportation},
year = {2025},
journal = {Communications in Transportation Research},
volume = {5},
number = {3},
pages = {100206},
keywords = {Cross-city, Transfer learning, Intelligent transportation, Smart city},
url = {https://www.sciopen.com/article/10.1016/j.commtr.2025.100206},
doi = {10.1016/j.commtr.2025.100206},
abstract = {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.}
}