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Sensors 2015, 15(7), 15974-15987; doi:10.3390/s150715974

Smart City Mobility Application—Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data

Department of Telecommunications and Information Processing, Gent University, St-Pietersnieuwstraat 41, Gent B-9000, Belgium
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Author to whom correspondence should be addressed.
Academic Editor: Antonio Puliafito
Received: 30 April 2015 / Revised: 22 June 2015 / Accepted: 29 June 2015 / Published: 3 July 2015
(This article belongs to the Special Issue Sensors and Smart Cities)
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Abstract

Mobility management represents one of the most important parts of the smart city concept. The way we travel, at what time of the day, for what purposes and with what transportation modes, have a pertinent impact on the overall quality of life in cities. To manage this process, detailed and comprehensive information on individuals’ behaviour is needed as well as effective feedback/communication channels. In this article, we explore the applicability of crowdsourced data for this purpose. We apply a gradient boosting trees algorithm to model individuals’ mobility decision making processes (particularly concerning what transportation mode they are likely to use). To accomplish this we rely on data collected from three sources: a dedicated smartphone application, a geographic information systems-based web interface and weather forecast data collected over a period of six months. The applicability of the developed model is seen as a potential platform for personalized mobility management in smart cities and a communication tool between the city (to steer the users towards more sustainable behaviour by additionally weighting preferred suggestions) and users (who can give feedback on the acceptability of the provided suggestions, by accepting or rejecting them, providing an additional input to the learning process). View Full-Text
Keywords: smart city; mobility management; modelling mobility decision making; gradient boosted trees; crowdsourcing smart city; mobility management; modelling mobility decision making; gradient boosted trees; crowdsourcing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Semanjski, I.; Gautama, S. Smart City Mobility Application—Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data. Sensors 2015, 15, 15974-15987.

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