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Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities
Capponi, Andrea; Vitello, Piergiorgio; Fiandrino, Claudio et al.
2019In IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 2019
Peer reviewed
 

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Keywords :
Mobile crowdsensing; machine learning; urban computing
Abstract :
[en] Urban planning typically relies on experience-based solutions and traditional methodologies to face urbanization issues and investigate the complex dynamics of cities. Recently, novel data-driven approaches in urban computing have emerged for researchers and companies. They aim to address historical urbanization issues by exploiting sensing data gathered by mobile devices under the so-called mobile crowdsensing (MCS) paradigm. This work shows how to exploit sensing data to improve traditionally experience-based approaches for urban decisions. In particular, we apply widely known Machine Learning (ML) techniques to achieve highly accurate results in predicting categories of local businesses (LBs) (e.g., bars, restaurants), and their attractiveness in terms of classes of temporal demands (e.g., nightlife, business hours). The performance evaluation is conducted in Luxembourg city and the city of Munich with publicly available crowdsensed datasets. The results highlight that our approach does not only achieve high accuracy, but it also unveils important hidden features of the interaction of citizens and LBs.
Disciplines :
Computer science
Author, co-author :
Capponi, Andrea ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Vitello, Piergiorgio ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Fiandrino, Claudio ;  IMDEA Networks Institute
Cantelmo, Guido ;  Technical University of Munich (TUM)
Kliazovich, Dzmitry ;  ExaMotive
Sorger, Ulrich ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Bouvry, Pascal ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
yes
Language :
English
Title :
Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities
Publication date :
July 2019
Event name :
IEEE Symposium on Computers and Communications (ISCC)
Event date :
July 2019
Audience :
International
Main work title :
IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 2019
Peer reviewed :
Peer reviewed
FnR Project :
FNR12252781 - Data-driven Computational Modelling And Applications, 2017 (01/09/2018-28/02/2025) - Andreas Zilian
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