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The Annotation of Global Positioning System (GPS) Data with Activity Purposes Using Multiple Machine Learning Algorithms

The Annotation of Global Positioning System (GPS) Data with Activity Purposes Using Multiple Machine Learning Algorithms

Sofie Reumers, Feng Liu, Davy Janssens, Geert Wets
Copyright: © 2014 |Pages: 15
ISBN13: 9781466661707|ISBN10: 1466661704|EISBN13: 9781466661714
DOI: 10.4018/978-1-4666-6170-7.ch008
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MLA

Reumers, Sofie, et al. "The Annotation of Global Positioning System (GPS) Data with Activity Purposes Using Multiple Machine Learning Algorithms." Mobile Technologies for Activity-Travel Data Collection and Analysis, edited by Soora Rasouli and Harry Timmermans, IGI Global, 2014, pp. 119-133. https://doi.org/10.4018/978-1-4666-6170-7.ch008

APA

Reumers, S., Liu, F., Janssens, D., & Wets, G. (2014). The Annotation of Global Positioning System (GPS) Data with Activity Purposes Using Multiple Machine Learning Algorithms. In S. Rasouli & H. Timmermans (Eds.), Mobile Technologies for Activity-Travel Data Collection and Analysis (pp. 119-133). IGI Global. https://doi.org/10.4018/978-1-4666-6170-7.ch008

Chicago

Reumers, Sofie, et al. "The Annotation of Global Positioning System (GPS) Data with Activity Purposes Using Multiple Machine Learning Algorithms." In Mobile Technologies for Activity-Travel Data Collection and Analysis, edited by Soora Rasouli and Harry Timmermans, 119-133. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6170-7.ch008

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Abstract

The aim of this chapter is to evaluate whether GPS data can be annotated or semantically enriched with different activity categories, allowing GPS data to be used in the future in simulation systems. The data in the study stems from a paper-and-pencil activity-travel diary survey and a corresponding survey in which GPS-enabled Personal Digital Assistants (PDAs) were used. A set of new approaches, which are all independent of additional sensor data and map information, thus significantly reducing additional costs and making the set of techniques relatively easily transferable to other regions, are proposed. Furthermore, this chapter makes a detailed comparison of different machine learning algorithms to semantically enrich GPS data with activity type information.

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