Personalized location prediction for group travellers from spatial–temporal trajectories

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


In recent years, research on location predictions by mining trajectories of users has attracted a lot of attentions. 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 preference of a travel group has significant impacts on the places they visit. 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 achieves significantly higher performance than previous location prediction methods.

Original languageEnglish
Pages (from-to)278-292
Number of pages15
JournalFuture Generation Computer Systems
Publication statusPublished - Jun 2018
Externally publishedYes


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

Cite this