GPS CaPPture: a System for GPS Trajectory Collection, Processing, and Destination Prediction

Description:

In the United States, smartphone ownership surpassed 69.5 million in February 2011 with a large portion of those users (20%) downloading applications (apps) that enhance the usability of a device by adding additional functionality. a large percentage of apps are written specifically to utilize the geographical position of a mobile device. One of the prime factors in developing location prediction models is the use of historical data to train such a model. with larger sets of training data, prediction algorithms become more accurate; however, the use of historical data can quickly become a downfall if the GPS stream is not collected or processed correctly. Inaccurate or incomplete or even improperly interpreted historical data can lead to the inability to develop accurately performing prediction algorithms. As GPS chipsets become the standard in the ever increasing number of mobile devices, the opportunity for the collection of GPS data increases remarkably. the goal of this study is to build a comprehensive system that addresses the following challenges: (1) collection of GPS data streams in a manner such that the data is highly usable and has a reduction in errors; (2) processing and reduction of the collected data in order to prepare it and make it highly usable for the creation of prediction algorithms; (3) creation of prediction/labeling algorithms at such a level that they are viable for commercial use. This study identifies the key research problems toward building the CaPPture (collection, processing, prediction) system.

Creator(s): Griffin, Terry W.
Creation Date: May 2012
Partner(s):
UNT Libraries
Collection(s):
UNT Theses and Dissertations
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Total Uses: 303
Past 30 days: 5
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Publisher Info:
Publisher Name: University of North Texas
Publisher Info: www.unt.edu
Place of Publication: Denton, Texas
Date(s):
  • Creation: May 2012
Description:

In the United States, smartphone ownership surpassed 69.5 million in February 2011 with a large portion of those users (20%) downloading applications (apps) that enhance the usability of a device by adding additional functionality. a large percentage of apps are written specifically to utilize the geographical position of a mobile device. One of the prime factors in developing location prediction models is the use of historical data to train such a model. with larger sets of training data, prediction algorithms become more accurate; however, the use of historical data can quickly become a downfall if the GPS stream is not collected or processed correctly. Inaccurate or incomplete or even improperly interpreted historical data can lead to the inability to develop accurately performing prediction algorithms. As GPS chipsets become the standard in the ever increasing number of mobile devices, the opportunity for the collection of GPS data increases remarkably. the goal of this study is to build a comprehensive system that addresses the following challenges: (1) collection of GPS data streams in a manner such that the data is highly usable and has a reduction in errors; (2) processing and reduction of the collected data in order to prepare it and make it highly usable for the creation of prediction algorithms; (3) creation of prediction/labeling algorithms at such a level that they are viable for commercial use. This study identifies the key research problems toward building the CaPPture (collection, processing, prediction) system.

Degree:
Discipline: Computer Science
Level: Doctoral
PublicationType: Disse
Language(s):
Subject(s):
Keyword(s): Location prediction | data mining | GPS | GIS
Contributor(s):
Partner:
UNT Libraries
Collection:
UNT Theses and Dissertations
Identifier:
  • ARK: ark:/67531/metadc115089
Resource Type: Thesis or Dissertation
Format: Text
Rights:
Access: Public
Holder: Griffin, Terry W.
License: Copyright
Statement: Copyright is held by the author, unless otherwise noted. All rights Reserved.