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A probabilistic kernel method for human mobility prediction with smartphones

Do, Trinh Minh Tri and Dousse, Olivier and Miettinen, Markus and Gatica-Perez, Daniel (2015):
A probabilistic kernel method for human mobility prediction with smartphones.
In: Pervasive and Mobile Computing, pp. 13-28, 20, DOI: 10.1016/j.pmcj.2014.09.001,
[Article]

Abstract

Human mobility prediction is an important problem that has a large number of applications, especially in context-aware services. This paper presents a study on location prediction using smartphone data, in which we address modeling and application aspects. Building personalized location prediction models from smartphone data remains a technical challenge due to data sparsity, which comes from the complexity of human behavior and the typically limited amount of data available for individual users. To address this problem, we propose an approach based on kernel density estimation, a popular smoothing technique for sparse data. Our approach contributes to existing work in two ways. First, our proposed model can estimate the probability that a user will be at a given location at a specific time in the future, by using both spatial and temporal information via multiple kernel functions. Second, we also show how our probabilistic framework extends to a more practical task of location prediction for a time window in the future. Our approach is validated on an everyday life location dataset consisting of 133 smartphone users. Our method reaches an accuracy of 84% for the next hour, and an accuracy of 77% for the next three hours.

Item Type: Article
Erschienen: 2015
Creators: Do, Trinh Minh Tri and Dousse, Olivier and Miettinen, Markus and Gatica-Perez, Daniel
Title: A probabilistic kernel method for human mobility prediction with smartphones
Language: German
Abstract:

Human mobility prediction is an important problem that has a large number of applications, especially in context-aware services. This paper presents a study on location prediction using smartphone data, in which we address modeling and application aspects. Building personalized location prediction models from smartphone data remains a technical challenge due to data sparsity, which comes from the complexity of human behavior and the typically limited amount of data available for individual users. To address this problem, we propose an approach based on kernel density estimation, a popular smoothing technique for sparse data. Our approach contributes to existing work in two ways. First, our proposed model can estimate the probability that a user will be at a given location at a specific time in the future, by using both spatial and temporal information via multiple kernel functions. Second, we also show how our probabilistic framework extends to a more practical task of location prediction for a time window in the future. Our approach is validated on an everyday life location dataset consisting of 133 smartphone users. Our method reaches an accuracy of 84% for the next hour, and an accuracy of 77% for the next three hours.

Journal or Publication Title: Pervasive and Mobile Computing
Volume: 20
Uncontrolled Keywords: ICRI-SC
Divisions: 20 Department of Computer Science
20 Department of Computer Science > System Security Lab
Profile Areas
Profile Areas > Cybersecurity (CYSEC)
Date Deposited: 04 Aug 2016 10:13
DOI: 10.1016/j.pmcj.2014.09.001
Identification Number: TUD-CS-2014-0977
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