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Mobile Information Systems
Volume 2017, Article ID 4043237, 11 pages
Research Article

Assessment of Smartphone Positioning Data Quality in the Scope of Citizen Science Contributions

1Department of Telecommunications and Information Processing, Ghent University, St-Pietersnieuwstraat 41, 9000 Ghent, Belgium
2Facultad de Ingeniería en Electricidad y Computación, Escuela Superior Politécnica del Litoral (ESPOL), Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador

Correspondence should be addressed to Angel J. Lopez; eb.tnegu@zepol.legna

Received 24 February 2017; Revised 8 May 2017; Accepted 24 May 2017; Published 21 June 2017

Academic Editor: Liang Chen

Copyright © 2017 Angel J. Lopez et al. 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.


Human travel behaviour has been addressed in many transport studies, where travel survey methods have been widely used to collect self-reported insights of daily mobility patterns. However, since the introduction of Global Navigation Satellite Systems (GNSS) and more recently smartphones with built-in GNSS, researchers have adopted these ubiquitous devices as tools for collecting mobility behaviour data. Although most studies recognize the applicability of this technology, it still has limitations. These are rarely addressed in a quantified manner. Often the quality of the collected data tends to be overestimated and these errors propagate into the aggregated results providing incomplete knowledge of the levels of confidence of the results and conclusions. In this study, we focus on the completeness aspects of data quality using GNSS data from four campaigns in the Flanders region of Belgium. The empirical results are based on mobility behaviour data collected through smartphones and include more than 450 participants over a period of twenty-nine months. Our findings show which transport mode is affected the most and how land use affects the quality of the collected data. In addition, we provide insights into the time to first fix that can be used for a better estimation of travel patterns.