Personalized location prediction for group travellers from spatial–temporal trajectories

Elahe Naserian, Xinheng Wang, Keshav Dahal, Zhi Wang, Zaijian Wang

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
165 Downloads (Pure)


In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly focus on individual movements, considering the trajectories as solo movements. However, a user usually does not visit locations just for the personal interest. The preferences of a travel group have significant impacts on the places they have visited. In this paper, we propose a novel personalized location prediction approach which further takes into account users’ travel group type. To achieve this goal, we propose a new group pattern discovery approach to extract the
travel groups from spatial-temporal trajectories of users. Type of the discovered groups, then, are identified through utilizing the profile information of the group members. The core idea underlying our proposal is the discovery of significant movement patterns of users to capture frequent movements by considering
the group types. Finally, the problem of location prediction is formulated as an estimation of the probability of a given user visiting a given location based on his/her current movement and his/her group type. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that investigates the influence of travel group type. By means
of a comprehensive evaluation using various datasets, we show that our proposed location prediction framework significantly achieve the higher performance than previous location prediction methods.
Original languageEnglish
Pages (from-to)278-292
Number of pages15
JournalFuture Generation Computer Systems
Early online date31 Jan 2018
Publication statusE-pub ahead of print - 31 Jan 2018


  • Personalized location prediction
  • Group pattern discovery
  • Trajectory mining
  • Frequent movement patterns


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