Leveraging periodicity in human mobility for next place prediction

Bhaskar Prabhala, Jingjing Wang, Budhaditya Deb, Thomas La Porta, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Periodic transitions from place to place are inherent in human movements. Through visual examination we detect these periodic movements in traces of user tracking data. However such user tracking data sets tend to be sparse and incomplete. In addition, periodic movements are surrounded by noise: transitions to and from less frequently visited places and transitions to one of a kind visits. In this paper, we present algorithms leveraging techniques and models to detect periodicity in individual user movements. Our algorithms predict a user's next place given only the current context of timestamp and location. We apply these algorithms to real user mobility data sets. Prediction accuracy depends on the ratio of periodic movements to noise in user traces. For majority of users in a movement tracking data set collected over a year, our algorithms achieve next place prediction accuracies of 50% and above.

Original languageEnglish (US)
Title of host publicationIEEE Wireless Communications and Networking Conference, WCNC
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2665-2670
Number of pages6
ISBN (Electronic)9781479930838
DOIs
StatePublished - Nov 10 2014
Event2014 IEEE Wireless Communications and Networking Conference, WCNC 2014 - Istanbul, Turkey
Duration: Apr 6 2014Apr 9 2014

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)1525-3511

Other

Other2014 IEEE Wireless Communications and Networking Conference, WCNC 2014
Country/TerritoryTurkey
CityIstanbul
Period4/6/144/9/14

ASJC Scopus subject areas

  • Engineering(all)

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