Decision Tree-Based Contextual Location Prediction from Mobile Device Logs

Linyuan Xia,Dongjin Wu,Qiumei Huang

doi:10.1155/2018/1852861

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.

Highlights

With the rapid development of mobile computing and positioning technology, it has made great progress in the ability and quality of location data acquisition
We demonstrate the feasibility of predicting contextual location from continuous mobile devices logs by machine learning techniques. e approach proposed in this paper includes three main modules: stay point detection, semantic places recognition, and decision tree-based prediction. e first module is applied to discover individuals’ behavioral sequence by extracting spatial feature from cluttered mobile device logs
We proposed a contextual location prediction framework for better personalized location recommendation in Locationbased services (LBS) by predicting personally semantic place from mobile devices logs

Summary

Introduction

With the rapid development of mobile computing and positioning technology, it has made great progress in the ability and quality of location data acquisition. Despite many years of research on location prediction issue, there are still some problems: (1) using raw location data without semantic information makes it hard to study personal purpose of daily route; (2) uncleaned check-in data from social platforms increase the cost of data process and analysis despite dispersed semantic information. To deal with these problems, a contextual location prediction framework is put forward in this paper.

Related Works

Contextual Location Prediction

Experiments and Results

Conclusions and Future Work