Context-aware location prediction

Roni Bar-David, Mark Last

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


Predicting the future location of mobile objects has become an important and challenging problem. With the widespread use of mobile devices, applications of location prediction include location-based services, resource allocation, handoff management in cellular networks, animal migration research, and weather forecasting. Most current techniques try to predict the next location of moving objects such as vehicles, people or animals, based on their movement history alone. However, ignoring the dynamic nature of mobile behavior may yield inaccurate predictions, at least part of the time. Analyzing movement in its context and choosing the best movement pattern by the current situation, can reduce some of the errors and improve prediction accuracy. In this chapter, we present a context-aware location prediction algorithm that utilizes various types of context information to predict future location of vehicles. We use five contextual features related to either the object environment or its current movement data: current location; object velocity; day of the week; weather conditions; and traffic congestion in the area. Our algorithm incorporates these context features into its trajectory-clustering phase as well as in its location prediction phase. We evaluate the proposed algorithm using two real-world GPS trajectory datasets. The experimental results demonstrate that the context-aware approach can significantly improve the accuracy of location predictions.

Original languageEnglish
Title of host publicationBig Data Analytics in the Social and Ubiquitous Context - 5th International Workshop on Modeling Social Media, MSM 2014 5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014 and First International Workshop on Machine Learning for Urban Sensor Data, SenseML 2014, Revised Selected Papers
EditorsFrederik Janssen, Alvin Chin, Martin Atzmueller, Christoph Trattner, Immanuel Schweizer
PublisherSpringer Verlag
Number of pages21
ISBN (Print)9783319290089
StatePublished - 1 Jan 2016
Event5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014 - Nancy, France
Duration: 15 Sep 201415 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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