A personal route prediction system based on trajectory data mining

Chen, Ling, Lv, Mingqi, Ye, Qian, Chen, Gencai and Woodward, John (2011) A personal route prediction system based on trajectory data mining. Information Sciences, 181 (7). pp. 1264-1284. ISSN 0020-0255

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/707279
Additional Information: Originally there would have been 24 month embargo
Keywords: Data mining; GPS; Route pattern; Route prediction; Privacy
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.1016/j.ins.2010.11.035
Depositing User: LIN, Zhiren
Date Deposited: 02 Nov 2017 08:18
Last Modified: 29 Apr 2020 14:55
URI: https://eprints.nottingham.ac.uk/id/eprint/47693

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