Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79706
Title: Predicting human location using correlated movements
Authors: Dao, Thi-Nga
Le, Duc Van
Yoon, Seokhoon
Keywords: DRNTU::Engineering::Computer science and engineering
Behavioral Pattern
Mobility Prediction
Issue Date: 2019
Source: Dao, T.-N., Le, D. V., & Yoon, S. (2019). Predicting human location using correlated movements. Electronics, 8(1), 54-. doi:10.3390/electronics8010054
Series/Report no.: Electronics
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.
URI: https://hdl.handle.net/10356/79706
http://hdl.handle.net/10220/49056
DOI: 10.3390/electronics8010054
Rights: © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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