A personal route prediction system based on trajectory data mining

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

Research output: Journal PublicationArticlepeer-review

95 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1264-1284
JournalInformation Sciences
Issue number7
Early online date7 Dec 2010
Publication statusPublished - 1 Apr 2011


  • Data mining
  • GPS
  • Privacy
  • Route pattern
  • Route prediction


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