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Electronics 2019, 8(1), 54; https://doi.org/10.3390/electronics8010054

Predicting Human Location Using Correlated Movements

1
Department of Electrical and Computer Engineering, University of Ulsan, Ulsan 44610, Korea
2
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Received: 24 October 2018 / Revised: 8 December 2018 / Accepted: 2 January 2019 / Published: 3 January 2019
(This article belongs to the Section Computer Science & Engineering)
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Abstract

This paper aims at estimating the current location, or predicting the next location, of a person when the recent location sequence of that person is unknown. Inspired by the fact that the behavior of an individual is greatly related to other people, a two-phase framework is proposed, which first finds persons who have highly correlated movements with a person-of-interest, then estimates the person’s location based on the position information for selected persons. For the first phase, we propose two methods: community interaction similarity-based (CISB) and behavioral similarity-based (BSB). The CISB method finds persons who have similar encounters with other members in the entire community. In the BSB method, members are selected if they show similar behavioral patterns with a given person, even though there are no direct encounters or evident co-locations between them. For the second phase, a neural network is considered in order to develop the prediction model based on the selected members. Evaluation results show that the proposed prediction model under the BSB scheme outperforms other methods, achieving top-1 accuracy of 71.13% and 69.36% for estimations of current and next locations, respectively, with the MIT dataset and 92.31% and 92.03% in case of the Dartmouth dataset. View Full-Text
Keywords: mobility prediction; behavioral pattern; tempo-spatial information; cellular network trace mobility prediction; behavioral pattern; tempo-spatial information; cellular network trace
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Dao, T.-N.; Le, D.V.; Yoon, S. Predicting Human Location Using Correlated Movements. Electronics 2019, 8, 54.

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