Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2018 (2018), Article ID 1852861, 11 pages
https://doi.org/10.1155/2018/1852861
Research Article

Decision Tree-Based Contextual Location Prediction from Mobile Device Logs

School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China

Correspondence should be addressed to Qiumei Huang; nc.ude.usys.2liam@muiqh

Received 22 November 2017; Accepted 25 February 2018; Published 1 April 2018

Academic Editor: Dik Lun Lee

Copyright © 2018 Linyuan Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Contextual location prediction is an important topic in the field of personalized location recommendation in LBS (location-based services). With the advancement of mobile positioning techniques and various sensors embedded in smartphones, it is convenient to obtain massive human mobile trajectories and to derive a large amount of valuable information from geospatial big data. Extracting and recognizing personally interesting places and predicting next semantic location become a research hot spot in LBS. In this paper, we proposed an approach to predict next personally semantic place with historical visiting patterns derived from mobile device logs. To address the problems of location imprecision and lack of semantic information, a modified trip-identify method is employed to extract key visit points from GPS trajectories to a more accurate extent while semantic information are added through stay point detection and semantic places recognition. At last, a decision tree model is adopted to explore the spatial, temporal, and sequential features in contextual location prediction. To validate the effectiveness of our approach, experiments were conducted based on a trajectory collection in Guangzhou downtown area. The results verified the feasibility of our approach on contextual location prediction from continuous mobile devices logs.