Unsupervised Learning for Human Mobility Behaviors

Siyuan Liu (), Shaojie Tang (), Jiangchuan Zheng () and Lionel M. Ni ()
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Siyuan Liu: Pennsylvania State University, State College, Pennsylvania 16801
Shaojie Tang: University of Texas at Dallas, Richardson, Texas 75080
Jiangchuan Zheng: Haitong International Securities Group Limited, Hong Kong
Lionel M. Ni: Hong Kong University of Science and Technology, Hong Kong

INFORMS Journal on Computing, 2022, vol. 34, issue 3, 1565-1586

Abstract: Learning human mobility behaviors from location-sensing data are crucial to mobility data mining because of its potential to address a range of analytical purposes in mobile context reasoning, including exploration, inference, and prediction. However, existing approaches suffer from two practical problems: temporal and spatial sparsity. To address these shortcomings, we present two unsupervised learning methods to model the mobility behaviors of multiple users (i.e., a population), considering efficiency and accuracy. These methods intelligently overcome the sparsity in individual data by seeking temporal commonality among users’ heterogeneous location behaviors. The advantages of our models are highlighted through experiments on several real-world mobility data sets, which also show how our methods can realize the three analytical purposes in a unified manner.

Keywords: human mobility behavior; unsupervised learning; mobile sensing; sparsity (search for similar items in EconPapers)
Date: 2022
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