A hierarchical hidden semi-markov model for modeling mobility data

Mitra Baratchi*, Nirvana Meratnia, Paul J. M. Havinga, Andrew K. Skidmore, Bert A. K. G. Toxopeus

*Corresponding author for this work

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

27 Citations (Scopus)

Abstract

Ubiquity of portable location-aware devices and popularity of online location-based services, have recently given rise to the collection of datasets with high spatial and temporal resolution. The subject of analyzing such data has consequently gained popularity due to numerous opportunities enabled by understanding objects' (people and animals, among others) mobility patterns. In this paper, we propose a hidden semi-Markov-based model to understand the behavior of mobile entities. The hierarchical state structure in our model allows capturing spatiotemporal associations in the locational history both at staypoints and on the paths connecting them. We compare the accuracy of our model with a number of other spatiotemporal models using two real datasets. Furthermore, we perform sensitivity analysis on our model to evaluate its robustness in presence of common issues in mobility datasets such as existence of noise and missing values. Results of our experiments show superiority of the proposed scheme compared with the other models.

Original languageEnglish
Title of host publicationUbiComp'14
Subtitle of host publicationproceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages401-412
Number of pages12
ISBN (Electronic)9781450329682
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014 - Seattle, United States
Duration: 13 Sep 201417 Sep 2014

Conference

Conference2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014
CountryUnited States
CitySeattle
Period13/09/1417/09/14

Keywords

  • Hidden semi-Markov model
  • mobility data analysis
  • movement modeling
  • movement prediction
  • next place prediction
  • Big data analytics

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