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25059 A hierarchical hidden semi-Markov model for modeling mobility data
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Baratchi, M. and Meratnia, N. and Havinga, P.J.M. and Skidmore, A.K. and Toxopeus, B.A.G (2014) A hierarchical hidden semi-Markov model for modeling mobility data. In: Proceedings of The 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2014), 13-15 Sept 2014, Seattle, WA, USA. pp. 401-412. ACM. ISBN 978-1-4503-2968-2

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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 spatio-temporal associations in the locational history both at stay-points and on the paths connecting them. We compare the accuracy of our model with a number of other spatio-temporal 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.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:COMMIT/SENSA: Sensor Networks for Public Safety
Uncontrolled Keywords:Hidden semi-Markov model; mobility data analysis; movement modeling; movement prediction; next place prediction; Big data analytics
ID Code:25059
Deposited On:09 December 2014
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