Learning trajectory information with neural networks and the markov model to develop intelligent location-based services

Sang Jun Han, Sung Bae Cho

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In the development of location-based services, various location-sensing techniques and experimental/commercial services have been used. However, conventional location-based services are limited in terms of flexibility because they depend on the current location of the user. We propose a novel method of predicting the user's future movements in order to develop advanced location-based services. The user's movement trajectory is modelled using a combination of recurrent self-organising maps (RSOM) and the Markov model. Future movement is predicted based on past movement trajectories. A prototype application based on location prediction is also presented. This application is a mobile user assistant targeted to university students. To verify the proposed method, a GPS dataset was collected on the Yonsei University campus. The results were promising enough to confirm that the application works flexibly even in ambiguous situations.

Original languageEnglish
Pages (from-to)291-301
Number of pages11
JournalJournal of Information and Knowledge Management
Volume5
Issue number4
DOIs
Publication statusPublished - 2006 Dec 1

Fingerprint

Location based services
neural network
Trajectories
Neural networks
learning
Self organizing maps
Global positioning system
Students
assistant
flexibility
university
student

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Library and Information Sciences

Cite this

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