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A personal route prediction system based on trajectory data mining

Chen, Ling; Lv, Mingqi; Ye, Qian; Chen, Gencai; Woodward, John


Ling Chen

Mingqi Lv

Qian Ye

Gencai Chen

John Woodward


This paper presents a system where the personal route of a user is predicted using a probabilistic model built from the historical trajectory data. Route patterns are extracted from personal trajectory data using a novel mining algorithm, Continuous Route Pattern Mining (CRPM), which can tolerate different kinds of disturbance in trajectory data. Furthermore, a client–server architecture is employed which has the dual purpose of guaranteeing the privacy of personal data and greatly reducing the computational load on mobile devices. An evaluation using a corpus of trajectory data from 17 people demonstrates that CRPM can extract longer route patterns than current methods. Moreover, the average correct rate of one step prediction of our system is greater than 71%, and the average Levenshtein distance of continuous route prediction of our system is about 30% shorter than that of the Markov model based method.


Chen, L., Lv, M., Ye, Q., Chen, G., & Woodward, J. (2011). A personal route prediction system based on trajectory data mining. Information Sciences, 181(7),

Journal Article Type Article
Acceptance Date Nov 27, 2010
Online Publication Date Dec 7, 2010
Publication Date Apr 1, 2011
Deposit Date Nov 2, 2017
Journal Information Sciences
Print ISSN 0020-0255
Electronic ISSN 0020-0255
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 181
Issue 7
Keywords Data mining; GPS; Route pattern; Route prediction; Privacy
Public URL
Publisher URL
Additional Information Originally there would have been 24 month embargo

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