Unsupervised Learning for Human Mobility Behaviors

Siyuan Liu, Shaojie Tang, Jiangchuan Zheng, Lionel M. Ni

Research output: Contribution to journalArticlepeer-review


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. Thesemethods 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.

Original languageEnglish (US)
Pages (from-to)1565-1586
Number of pages22
JournalINFORMS Journal on Computing
Issue number3
StatePublished - May 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research


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