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Journal of Advanced Transportation
Volume 2018, Article ID 8710402, 12 pages
https://doi.org/10.1155/2018/8710402
Research Article

Supervised Land Use Inference from Mobility Patterns

1Transportation Engineering Unit, AICIA, Camino de los Descubrimientos, s/n, 41092 Seville, Spain
2Transportation Engineering, Faculty of Engineering, University of Seville, Camino de los Descubrimientos, s/n, 41092 Seville, Spain

Correspondence should be addressed to Noelia Caceres; se.su.iste@serecacn

Received 29 November 2017; Revised 19 March 2018; Accepted 11 April 2018; Published 21 May 2018

Academic Editor: Giulio E. Cantarella

Copyright © 2018 Noelia Caceres and Francisco G. Benitez. 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.

Abstract

This paper addresses the relationship between land use and mobility patterns. Since each particular zone directly feeds the global mobility once acting as origin of trips and others as destination, both roles are simultaneously used for predicting land uses. Specifically this investigation uses mobility data derived from mobile phones, a technology that emerges as a useful, quick data source on people’s daily mobility, collected during two weeks over the urban area of Malaga (Spain). This allows exploring the relevance of integrating weekday-weekend trip information to better determine the category of land use. First, this work classifies patterns on trips originated and terminated in each zone into groups by means of a clustering approach. Based on identifiable relationships between activity and times when travel peaks appear, a preliminary categorization of uses is provided. Then, both grouping results are used as input variables in a K-nearest neighbors (KNN) classification model to determine the exact land use. The KNN method assumes that the category of an object must be similar to the category of the closest neighbors. After training the models, the findings reveal that this approach provides a precise land use categorization, yielding the best accuracy results for the major categories of land uses in the studied area. Moreover, as a result, the weekend data certainly contributes to finding more precise land uses as those obtained by just weekday data. In particular, the percentage of correctly predicted categories using both weekday and weekend is around 80%, while just weekday data reach 67%. The comparison with actual land uses also demonstrates that this approach is able to provide useful information, identifying zones with a specific clear dominant use (residential, industrial, and commercial), as well as multiactivity zones (mixed). This fact is especially useful in the context of urban environments where multiple activities coexist.