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|Title:||AI and deep learning for urban computing||Authors:||Wang, S
|Issue Date:||2021||Source:||In Shi W., Goodchild M.F., Batty M., Kwan MP., Zhang A. (Eds. ), Urban Informatics, p. 815-844. Singapore: Springer, 2021||Abstract:||In the big data era, with the large volume of available data collected by various sensors deployed in urban areas and the recent advances in AI techniques, urban computing has become increasingly important to facilitate the improvement of people’s lives, city operation systems, and the environment. In this chapter, we introduce the challenges, methodologies, and applications of AI techniques for urban computing. We first introduce the background, followed by listing key challenges from the perspective of computer science when AI techniques are applied. Then we briefly introduce the AI techniques that are widely used in urban computing, including supervised learning, semi-supervised learning, unsupervised learning, matrix factorization, graphic models, deep learning, and reinforcement learning. With the recent advances of deep-learning techniques, models such as CNN and RNN have shown significant performance gains in many applications. Thus, we briefly introduce the deep-learning models that are widely used in various urban-computing tasks. Finally, we discuss the applications of urban computing including urban planning, urban transportation, location-based social networks (LBSNs), urban safety and security, and urban-environment monitoring. For each application, we summarize major research challenges and review previous work that uses AI techniques to address them.||ISBN:||978-981-15-8982-9 (Print ISBN)
978-981-15-8983-6 (Online ISBN)
|DOI:||10.1007/978-981-15-8983-6_43||Rights:||© The Author(s) 2021
Open AccessThis chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The following publication Wang S., Cao J. (2021) AI and Deep Learning for Urban Computing. In: Shi W., Goodchild M.F., Batty M., Kwan MP., Zhang A. (eds) Urban Informatics. The Urban Book Series. Springer, Singapore is available at https://doi.org/10.1007/978-981-15-8983-6_43
|Appears in Collections:||Book Chapter|
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Citations as of Jun 26, 2022
Citations as of Jun 26, 2022
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