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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17813

Title: The Annotation of Global Positioning System (GPS) Data with Activity Purposes Using Multiple Machine Learning Algorithms
Authors: REUMERS, Sofie
LIU, Feng
WETS, Geert
Issue Date: 2014
Publisher: IGI Global
Citation: Rasouli, Soora; Timmermans, Harry (Ed.). Mobile Technologies for Activity-Travel Data Collection and Analysis, p. 119-133
Series/Report: Advances in Data Mining and Database Management (ADMDM) Book Series
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.
URI: http://hdl.handle.net/1942/17813
DOI: 10.4018/978-1-4666-6170-7.ch008
ISI #: 000363211700010
ISBN: 9781466661707
Category: B2
Type: Book Section
Validation: vabb, 2017
Appears in Collections: Research publications

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