Discovery of loose travelling companion patterns from human trajectories

Elahe Naserian, Xinheng Wang, Xiaolong Xu, Yuning Dong

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

10 Citations (Scopus)

Abstract

Discovery of useful patterns from human movement behavior can convey valuable knowledge to a variety of critical applications. Existing approaches focus on outdoor group discovery and mainly consider objects who belong to the same cluster as a possible group, which leads to the inability to discover all the existing groups. This is especially true for indoor human-generated trajectories, where spatially distant objects could be related to one group. Considering the human movement characteristic, we propose the loose travelling companion pattern which allows objects in different clusters to form a group, as long as the community of clusters doesn't change during the movement and all members stay together in the limited number of times. To tolerate the unrealistic temporary clusters, we extend the algorithm to the weakly consistent travelling companion pattern which relaxes the continuous requirement. In this paper, we also introduce a smart trolley which is used to collect the passenger movement data at airports in order to discover the groups. The acquired knowledge will then be applied to provide personalized services and advertisement. By the experimental analysis and comparison with the real and synthetic datasets, it is shown that the proposed approach can discover more complete and accurate groups.
Original languageEnglish
Title of host publication2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
PublisherIEEE
Pages1238-1245
Number of pages8
ISBN (Electronic)9781509042975
ISBN (Print)9781509042982
DOIs
Publication statusPublished - 14 Dec 2016

Keywords

  • human trajectories
  • movement pattern
  • spatio-temporal data mining
  • group relationship

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