Intelligent GPS trace management for human mobility pattern detection

Large volumes of volunteered GPS traces in the last decade have provided location-based services with an opportunity to become more intelligent and personalized. Individual and group mobility patterns, detected from GPS traces, can be used for this purpose. In this paper, we show the potential of GP...

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Bibliographic Details
Published in:Cogent engineering 2017-01, Vol.4 (1), p.1390813
Main Author: Hashemi, Mahdi
Format: Article
Language:English
Subjects:
Artificial intelligence
crowd-sourcing
Data base management systems
Data mining
intelligent transportation
Interactive systems
Location based services
Machine learning
Pattern recognition
spatial-temporal data mining
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Summary:Large volumes of volunteered GPS traces in the last decade have provided location-based services with an opportunity to become more intelligent and personalized. Individual and group mobility patterns, detected from GPS traces, can be used for this purpose. In this paper, we show the potential of GPS traces, if managed properly in the database, for detecting points of interest for individual users and even recognizing individual users from their walking patterns. However, when it comes to GPS traces, databases can be very complicated and cumbersome to populate. Databases provided by OSM and GeoLife do not effectively pave the path for data mining and machine learning techniques which require a much more detailed and organized database. A GPS trace database must provide statistics and detailed information about GPS traces not only for visualization purposes at the front-end, but also for cross checking purposes to eliminate erroneous records and to be applied in mobility pattern detection applications. This study provides the design of an interactive database management system for GPS traces whose applications in detecting points of interest and user identification are tested with GPS traces from the GeoLife project. The results show that while the accuracy of detected points of interest depends mostly on the size of data, the accuracy of user identification relies more upon the appropriate choice of input features to machine learning techniques.
ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2017.1390813