Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79508
Title: Privacy-preserving trajectory classification of driving trip data based on pattern discovery techniques
Authors: Wu, GPK 
Chan, KCC 
Keywords: Spatio-temporal data mining
Trajectory classification
Urban traffic analytics
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018, v. 2018-January, p. 3816-3825 How to cite?
Abstract: With the rapid growth of the remote sensing technology and its high adoption in automotive domain, identifying patterns in the context of driving trips becomes a promising and interesting area of research and application. Due to privacy concern, user location data in the moving object trajectory are to be anonymized before publishing. To classify the privacy-preserving driving trips in a set of recorded GPS tracks, this paper presents an information theoretic approach to characterize them based on their occurrences of frequently detected patterns. The patterns are discovered through a statistical significance test on a generated set of spatio-temporal data and its associated attributes that represent the characteristics of recorded GPS data. For evaluating the performance of the proposed approach, a real dataset with class information is tested to validate its classificatory power and compare with other approaches. The result indicates the approach is effective and efficient in achieving a good accuracy in the prediction of the class labels of the different driving trips based on the transformed set of attributes.
Description: 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 11-14 December 2017
URI: http://hdl.handle.net/10397/79508
ISBN: 9781538627143
DOI: 10.1109/BigData.2017.8258383
Appears in Collections:Conference Paper

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