Deep Air – A Smart City AI Synthetic Data Digital Twin Solving the Scalability Data Problems
Author
Other authors
Publication date
2022ISSN
0922-6389
Abstract
Cities are becoming data-driven, re-engineering their processes to adapt to dynamically changing needs. A.I. brings new capabilities, effectively enlarging the space of policy interventions that can be explored and applied. Therefore, new tools are needed to augment our capacity to traverse this space and find adequate policy interventions. Digital twins are revealing themselves as powerful tools for policy experimentation and exploration, allowing faster and more complete explorations while avoiding costly interventions. However, they face some problems, among them data availability and model scalability. We introduce a digital twin framework based on an A.I. and a synthetic data model on NO2 pollution as a proof-of-concept, showing that this approach is feasible for policy evaluation and (autonomous) intervention and solves the problems of data scarcity and model scalability while enabling city level Open Innovation.
Document Type
Article
Document version
Published version
Language
English
Keywords
Digital Twins
Pages
4 p.
Publisher
IOS Press BV
Is part of
Frontiers in Artificial Intelligence and Applications
This item appears in the following Collection(s)
Rights
© L'autor/a
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc/4.0/