A personal route prediction system based on trajectory data mining

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

Abstract

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.

Citation

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), https://doi.org/10.1016/j.ins.2010.11.035

• 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: 1872-6291
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 181
• Issue: 7
• DOI: https://doi.org/10.1016/j.ins.2010.11.035
• Keywords: Data mining; GPS; Route pattern; Route prediction; Privacy
• Public URL: https://nottingham-repository.worktribe.com/output/707279
• Publisher URL: https://doi.org/10.1016/j.ins.2010.11.035
• Additional Information: Originally there would have been 24 month embargo
• Contract Date: Oct 31, 2017