QROWD: a platform for integrating citizens in smart city data analytics
QROWD: a platform for integrating citizens in smart city data analytics
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.
285-321
Ibáñez, L.-D.
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Maddalena, E.
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Gomer, R.
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Simperl, E.
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Zeni, M.
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Bignotti, E.
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Chenu-Abente, R.
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Giunchiglia, F.
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Westphal, P.
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Stadler, C.
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Dziwis, G.
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Lehmann, J.
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Yumusak, S.
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Voigt, M.
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Sanguino, M.-A.
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Villazán, J.
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Ruiz, R.
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Ibáñez, L.-D.
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Maddalena, E.
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Gomer, R.
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Simperl, E.
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Zeni, M.
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Bignotti, E.
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Chenu-Abente, R.
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Giunchiglia, F.
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Westphal, P.
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Stadler, C.
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Dziwis, G.
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Lehmann, J.
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Yumusak, S.
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Voigt, M.
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Sanguino, M.-A.
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Villazán, J.
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Ruiz, R.
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Pariente-Lobo, T.
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Ibáñez, L.-D., Maddalena, E., Gomer, R., Simperl, E., Zeni, M., Bignotti, E., Chenu-Abente, R., Giunchiglia, F., Westphal, P., Stadler, C., Dziwis, G., Lehmann, J., Yumusak, S., Voigt, M., Sanguino, M.-A., Villazán, J., Ruiz, R. and Pariente-Lobo, T.
(2022)
QROWD: a platform for integrating citizens in smart city data analytics.
In,
Singh, Pradeep Kumar, Paprzycki, Marcin, Essaaidi, Mohamad and Rahimi, Shahram
(eds.)
Sustainable Smart Cities : Theoretical Foundations and Practical Considerations.
(Studies in Computational Intelligence, 942)
1st ed.
Springer Cham, .
(doi:10.1007/978-3-031-08815-5_16).
Record type:
Book Section
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.
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More information
e-pub ahead of print date: 3 November 2022
Additional Information:
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.
Identifiers
Local EPrints ID: 478346
URI: http://eprints.soton.ac.uk/id/eprint/478346
ISSN: 1860-949X
PURE UUID: b1cc2a34-8776-47d0-9ed7-5dabf6ea834f
Catalogue record
Date deposited: 28 Jun 2023 16:58
Last modified: 17 Mar 2024 03:44
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Contributors
Author:
L.-D. Ibáñez
Author:
R. Gomer
Author:
M. Zeni
Author:
E. Bignotti
Author:
R. Chenu-Abente
Author:
F. Giunchiglia
Author:
P. Westphal
Author:
C. Stadler
Author:
G. Dziwis
Author:
J. Lehmann
Author:
S. Yumusak
Author:
M. Voigt
Author:
M.-A. Sanguino
Author:
J. Villazán
Author:
R. Ruiz
Author:
T. Pariente-Lobo
Editor:
Pradeep Kumar Singh
Editor:
Marcin Paprzycki
Editor:
Mohamad Essaaidi
Editor:
Shahram Rahimi
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