King's College London

Research portal

QROWD—A Platform for Integrating Citizens in Smart City Data Analytics

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Luis Daniel Ibáñez, Eddy Maddalena, Richard Gomer, Elena Simperl, Mattia Zeni, Enrico Bignotti, Ronald Chenu-Abente, Fausto Giunchiglia, Patrick Westphal, Claus Stadler, Gordian Dziwis, Jens Lehmann, Semih Yumusak, Martin Voigt, Maria Angeles Sanguino, Javier Villazán, Ricardo Ruiz, Tomas Pariente-Lobo

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Science and Business Media Deutschland GmbH
Pages285-321
Number of pages37
DOIs
Published2023

Publication series

NameStudies in Computational Intelligence
Volume942
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Bibliographical note

Funding Information: Acknowledgements Research on this paper was supported by the QROWD project, part of the Horizon 2020 programme under grant agreement 732194. We also acknowledge the Smart City managers of the Municipality of Trento. Funding Information: Research on this paper was supported by the QROWD project, part of the Horizon 2020 programme under grant agreement 732194. We also acknowledge the Smart City managers of the Municipality of Trento. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

King's Authors

Abstract

Optimizing mobility services is one of the greatest challenges Smart Cities face in their efforts to improve residents’ wellbeing and reduce emissions. The advent of IoT has created unparalleled opportunities to collect large amounts of data about how people use transportation. This data could be used to ascertain the quality and reach of the services offered and to inform future policy—provided cities have the capabilities to process, curate, integrate and analyse the data effectively. At the same time, to be truly ‘Smart’, cities need to ensure that the data-driven decisions they make reflect the needs of their citizens, create feedback loops, and widen participation. In this chapter, we introduce QROWD, a data integration and analytics platform that seamlessly integrates multiple data sources alongside human, social and computational intelligence to build hybrid, automated data-centric workflows. By doing so, QROWD applications can take advantage of the best of both worlds: the accuracy and scale of machine computation, and the skills, knowledge and expertise of people. We present the architecture and main components of the platform, as well as its usage to realise two mobility use cases: estimating the modal split, which refers to trips people take that involve more than one type of transport, and urban auditing.

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454