A personal route prediction system based on trajectory data mining

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 di...

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Main Authors: Chen, Ling, Lv, Mingqi, Ye, Qian, Chen, Gencai, Woodward, John
Format: Article
Published: Elsevier 2011
Subjects:
Online Access:https://eprints.nottingham.ac.uk/47693/
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author Chen, Ling
Lv, Mingqi
Ye, Qian
Chen, Gencai
Woodward, John
author_facet Chen, Ling
Lv, Mingqi
Ye, Qian
Chen, Gencai
Woodward, John
author_sort Chen, Ling
building Nottingham Research Data Repository
collection Online Access
description 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.
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institution University of Nottingham Malaysia Campus
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publishDate 2011
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spelling nottingham-476932020-04-29T14:55:34Z https://eprints.nottingham.ac.uk/47693/ A personal route prediction system based on trajectory data mining Chen, Ling Lv, Mingqi Ye, Qian Chen, Gencai Woodward, John 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. Elsevier 2011-04-01 Article PeerReviewed 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 Data mining; GPS; Route pattern; Route prediction; Privacy https://doi.org/10.1016/j.ins.2010.11.035 doi:10.1016/j.ins.2010.11.035 doi:10.1016/j.ins.2010.11.035
spellingShingle Data mining; GPS; Route pattern; Route prediction; Privacy
Chen, Ling
Lv, Mingqi
Ye, Qian
Chen, Gencai
Woodward, John
A personal route prediction system based on trajectory data mining
title A personal route prediction system based on trajectory data mining
title_full A personal route prediction system based on trajectory data mining
title_fullStr A personal route prediction system based on trajectory data mining
title_full_unstemmed A personal route prediction system based on trajectory data mining
title_short A personal route prediction system based on trajectory data mining
title_sort personal route prediction system based on trajectory data mining
topic Data mining; GPS; Route pattern; Route prediction; Privacy
url https://eprints.nottingham.ac.uk/47693/
https://eprints.nottingham.ac.uk/47693/
https://eprints.nottingham.ac.uk/47693/