Reference : Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Sma...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/39439
Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities
English
Capponi, Andrea mailto [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) >]
Jul-2019
IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 2019
Yes
International
IEEE Symposium on Computers and Communications (ISCC)
July 2019
[en] Mobile crowdsensing ; machine learning ; urban computing
[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.
http://hdl.handle.net/10993/39439

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