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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 (Hasselt University, Belgium), Feng Liu (Hasselt University, Belgium), Davy Janssens (Hasselt University, Belgium), and Geert Wets (Hasselt University, Belgium)
Copyright: © 2014 |Pages: 15
DOI: 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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Literature

This section begins by reviewing the literature concerning traditional travel survey methods, and current technological advancements in travel data collection with a particular focus on GPS-based surveys. In the second part of the literature review, we take a closer look at studies in which machine learning algorithms and artificial intelligence techniques are applied to automatically infer activity and travel attributes from raw GPS data.

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