Empowered by Internet of Things (IoT) technologies and advanced algorithms that can collect and handle massive data sets, urban computing and intelligence can make more informed decisions and create feedback loops between humans and the urban environment. It can bridge the gaps between ubiquitous sensing, intelligent computing, cooperative communication, and big data management technologies to create novel solutions which can improve urban environments, quality of life, and smart city systems. Urban computing and intelligence has recently attracted extensive attention from both industry and academia for tackling many problems resulting from rapid urbanization, including transportation, environment, and energy issues.

Various learning architectures and techniques, such as machine learning, representation learning, deep learning, and transfer learning, have been introduced to revolutionize big social data mining and information processing methods. Both traditional learning methods and advanced learning methods are essential to meet the needs of urban data acquisition, storage, management, processing, and analysis. Making full use of learning methods can empower the city to be smart enough to efficiently handle large volumes of urban data.

In light of this potential, this Special Issue provides a venue for promoting urban computing and intelligence based on diverse learning methods. We welcome high-quality original research and review articles which showcase potential applications of learning methods and algorithms, including the intelligent environment, smart transportation, intelligent energy management, and big-data-driven urban planning. Even though these approaches have achieved certain success, various scientific and engineering challenges still need to be addressed, such as software and hardware development, computational complexity, data multi-source heterogeneity, and security/privacy problems. We therefore welcome research contributions that seek to tackle these issues.

Potential topics include but are not limited to the following:

• Learning methods for urban data mining and analysis
• Novel machine learning methods for urban data clustering and classification
• Data mining and machine learning for smart cities
• Security, trust, and privacy of urban computing
• Artificial intelligence models for urban computing and intelligence
• Big data Infrastructures for urban analytics
• Urban sensing and city intelligent sensing
• Personalized recommendation systems based on urban data
• Urban environment monitoring, analytics, and prediction
• Advanced learning methods for intelligent transportation systems