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Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities

Capponi, Andrea and Vitello, Piergiorgio and Fiandrino, Claudio and Cantelmo, Guido and Kliazovich, Dzmitry and Sorger, Ulrich and Bourvry, Pascal (2019) Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities. In: IEEE Symposium on Computers and Communications (ISCC), June 30 - July 3rd, 2019, Barcellona.

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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.

Item Type: Conference or Workshop Papers (Paper)
Depositing User: Claudio Fiandrino
Date Deposited: 24 Sep 2019 07:08
Last Modified: 24 Sep 2019 07:08

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