image: The systematic CCTL framework consists of four essential steps: data fetching and preprocessing, cross-domain linking and feature alignment, model refinement and generalization, and deployment with continuous learning.
Credit: Communications in Transportation Research
As urbanization accelerates globally and cities face mounting pressure to become both smarter and more sustainable, a fundamental question emerges: How can cities effectively learn from each other’s experiences to overcome data limitations and accelerate urban innovation?
To answer this question, researchers at Shanghai University, Tsinghua University and Northeastern University have conducted the first comprehensive review of Cross-City Transfer Learning (CCTL) in urban computing, systematically analyzing how knowledge transfer between cities can address critical challenges in smart city development and sustainable transportation.
They published their study on 3 September 2025, in Communications in Transportation Research.
“Despite the growing importance of CCTL, no dedicated review had systematically examined its applications and challenges. Our work fills this crucial gap by providing a holistic overview that bridges urban computing and transfer learning,” explains Ying Yang, a professor at the School of Management at Shanghai University.
Mapping the landscape of cross-city knowledge transfer
The review systematically examines CCTL applications across three major urban computing domains: prediction tasks (traffic flow forecasting, air quality monitoring), detection tasks (crime prediction, traffic accident detection), and deployment tasks (facility location planning, infrastructure optimization).
Through extensive literature analysis, the researchers identified common patterns and challenges across different applications. “We found that while cities share similar urban management objectives, significant variations in infrastructure, policy environments, and socioeconomic characteristics create substantial domain gaps that current methods struggle to bridge effectively,” Jiahao Zhan, a Ph.D. majoring in management and science engineering, says.
Policy transfer emerges as critical application
Beyond technical applications, the review highlights the growing importance of cross-city policy transfer for sustainable urban development. Cities can learn from successful low-carbon transportation policies, public transit initiatives, and shared mobility programs implemented elsewhere, significantly reducing implementation risks and accelerating the transition to climate-neutral transport systems.
Identifying future research directions
The comprehensive analysis reveals five major challenges: data heterogeneity and integration difficulties, limited model adaptability across diverse urban contexts, environmental and infrastructure differences between cities, computational resource constraints, and privacy concerns in cross-city data sharing.
“Our review establishes CCTL as a critical research domain and provides researchers with practical guidance on datasets, methods, and evaluation metrics. As cities worldwide pursue climate neutrality goals, the ability to systematically learn from each other’s experiences will become increasingly vital,” explains Yang Liu, an Associate Research Professor at the School of Vehicle and Mobility, Tsinghua University.
The above research is published in Communications in Transportation Research (COMMTR), which is a fully open access journal co-published by Tsinghua University Press and Elsevier. COMMTR publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. COMMTR is also among the first transportation journals to make the Replication Package mandatory to facilitate researchers, practitioners, and the general public in understanding and advancing existing knowledge. At its discretion, Tsinghua University Press will pay the open access fee for all published papers in 2025.
About Communications in Transportation Research
Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University and Professor Shuai’an Wang from Hong Kong Polytechnic University. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems, aiming to serve as an international platform for showcasing and exchanging innovative achievements in transportation and related fields, fostering academic exchange and development between China and the global community.
It has been indexed in SCIE, SSCI, Ei Compendex, Scopus, CSTPCD, CSCD, OAJ, DOAJ, TRID and other databases. It was selected as Q1 Top Journal in the Engineering and Technology category of the Chinese Academy of Sciences (CAS) Journal Ranking List. In 2022, it was selected as a High-Starting-Point new journal project of the “China Science and Technology Journal Excellence Action Plan”. In 2024, it was selected as the Support the Development Project of “High-Level International Scientific and Technological Journals”. The same year, it was also chosen as an English Journal Tier Project of the “China Science and Technology Journal Excellence Action Plan Phase Ⅱ”. In 2024, it received the first impact factor (2023 IF) of 12.5, ranking Top1 (1/58, Q1) among all journals in “TRANSPORTATION” category. In 2025, its 2024 IF was announced as 14.5, maintaining the Top 1 position (1/61, Q1) in the same category. Tsinghua University Press will cover the open access fee for all published papers in 2025.
Journal
Communications in Transportation Research
Article Title
Cross-city transfer learning: Applications and challenges towards smart cities and sustainable transportation
Article Publication Date
3-Sep-2025