Smart City Development with Urban Transfer Learning
This provides practical guidelines for city planners and practitioners to apply transfer learning in smart city domains like public safety and transportation, though it is incremental as it adapts existing transfer learning methods to this specific context.
The paper addresses the cold-start problem in smart city development where cities with limited data struggle to implement services, proposing urban transfer learning as a solution to accelerate development by transferring knowledge from data-rich cities.
Nowadays, the smart city development levels of different cities are still unbalanced. For a large number of cities which just started development, the governments will face a critical cold-start problem: 'how to develop a new smart city service with limited data?'. To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm. This article investigates the common process of urban transfer learning, aiming to provide city planners and relevant practitioners with guidelines on how to apply this novel learning paradigm. Our guidelines include common transfer strategies to take, general steps to follow, and case studies in public safety, transportation management, etc. We also summarize a few research opportunities and expect this article can attract more researchers to study urban transfer learning.