Human mobility-pattern discovery and next-place prediction from GPS data

Faina Khoroshevsky, Boaz Lerner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We provide a novel algorithm for the discovery of mobility patterns and prediction of users’ destination locations, both in terms of geographic coordinates and semantic meaning. We did not use any semantic data voluntarily provided by a user, and there was no sharing of data among the users. An advantage of our algorithm is that it allows a trade-off between prediction accuracy and information. Experimental validation was conducted on a GPS dataset collected in the Microsoft Research Asia GeoLife project by 168 users in a period of over five years.

Original languageEnglish
Title of host publicationMultimodal Pattern Recognition of Social Signals in Human-Computer-Interaction - 4th IAPR TC 9 Workshop, MPRSS 2016, Revised Selected Papers
EditorsStefan Scherer, Friedhelm Schwenker
PublisherSpringer Verlag
Pages24-35
Number of pages12
ISBN (Print)9783319592589
DOIs
StatePublished - 1 Jan 2017
Event4th IAPR TC 9 Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016 - Cancun, Mexico
Duration: 4 Dec 20164 Dec 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10183 LNAI

Conference

Conference4th IAPR TC 9 Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016
Country/TerritoryMexico
CityCancun
Period4/12/164/12/16

Keywords

  • GeoLife
  • Human behavior
  • Location extraction
  • Mobility pattern
  • Next place prediction
  • Positioning technology
  • Semantic information
  • Stay point
  • Trajectory data

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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