Supporting serendipitous social interaction using human mobility prediction

Zhiwen Yu, Hui Wang, Bin Guo, Tao Gu, Tao Mei

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Leveraging the regularities of people's trajectories, mobility prediction can help forecast social interaction opportunities. In this paper, in order to facilitate real-world social interaction, we aim to predict “serendipitous” social interactions, which are defined as unplanned encounters and interaction opportunities and regarded as emerging social interactions. We collected GPS trajectory data from people' daily life on campus and use it as empirical mobility traces to generate decision trees and model trees to predict next venues, arrival times, and user encounter. Mobility regularities are mainly considered in these prediction models, and mobility contexts (e.g., time, location, and speed) act as decision nodes in the classification trees. Experimental results using collected GPS data showed that our system achieves 90% accuracy for predicting a user's next venue using a decision tree algorithm, with minute-level (around 5 min) prediction error for arrival time using the model tree algorithm. Two prototype applications were developed to support serendipitous social interaction on campus, and the feedback from a user study with 25 users demonstrated the usability of these two applications.
Original languageEnglish
Pages (from-to)811-818
Number of pages8
JournalIEEE Transactions on Human-Machine Systems
Issue number6
Publication statusPublished - Dec 2015
Externally publishedYes


  • GPS data
  • inference model
  • mobility prediction
  • serendipitous social interaction
  • user study


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