Personalized location prediction for group travellers from spatial-temporal trajectories

Naserian, Elahe, Wang, Xinheng ORCID:, Dahal, Keshav, Wang, Zhi and Wang, Zaijian (2018) Personalized location prediction for group travellers from spatial-temporal trajectories. Future Generation Computer Systems, 83. pp. 278-292. ISSN 0167-739X

[thumbnail of Naserian-etal-2018-Personalized-location-prediction-for-group-travellers.pdf]
Naserian-etal-2018-Personalized-location-prediction-for-group-travellers.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (3MB) | Preview


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 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 achieves significantly higher performance than previous location prediction methods.

Item Type: Article
Identifier: 10.1016/j.future.2018.01.024
Additional Information: © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Keywords: Personalized location prediction, group pattern discovery ,trajectory mining, frequent movement patterns.
Subjects: Computing > Systems > Computer networking
Depositing User: Henry Wang
Date Deposited: 30 Jan 2018 16:43
Last Modified: 06 Feb 2024 15:55


Downloads per month over past year

Actions (login required)

View Item View Item