Next Article in Journal
Efficient Pilot Decontamination Schemes in 5G Massive MIMO Systems
Previous Article in Journal
Comparing Accuracy of Three Methods Based on the GIS Environment for Determining Winching Areas
Article Menu

Export Article

Open AccessFeature PaperArticle
Electronics 2019, 8(1), 54;

Predicting Human Location Using Correlated Movements

Department of Electrical and Computer Engineering, University of Ulsan, Ulsan 44610, Korea
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)
Full-Text   |   PDF [1193 KB, uploaded 3 January 2019]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Dao, T.-N.; Le, D.V.; Yoon, S. Predicting Human Location Using Correlated Movements. Electronics 2019, 8, 54.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top