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Characterizing and modeling people movement from mobile phone sensing traces

Vu, Long and Nguyen, Phuong and Nahrstedt, Klara and Richerzhagen, Björn :
Characterizing and modeling people movement from mobile phone sensing traces.
[Online-Edition: http://www.sciencedirect.com/science/article/pii/S1574119214...]
In: Elsevier Pervasive and Mobile Computing Journal
[Article] , (2014)

Official URL: http://www.sciencedirect.com/science/article/pii/S1574119214...

Abstract

With the ubiquity of mobile phones, a high accuracy of characterizing and modeling people movement is achievable. The knowledge about people's mobility enables many applications including highly efficient planning of cities resources and network infrastructures, or dissemination of safety alerts. However, characterizing and modeling people movement remain very challenging due to difficulties in (a) capturing, cleaning, analyzing and storing real traces, and (b) achieving accurate predictions of different future contexts.

In this paper, we present our effort in measuring and capturing phone sensory data as real traces, cleaning up measurements, and constructing prediction models. Specifically, we discuss design methodology, learned lessons from the implementation and deployment of a large-scale scanning system on 123 Google Android phones for 6 months at University of Illinois campus. We also conduct a characterization study on collected traces and present new findings in location visit pattern, location popularity, and contact pattern. Finally, we exploit joint location/contact traces to derive: (1) predictive models of missing contacts, and (2) prediction framework that provides future contextual information of people movement including locations, stay duration, and social contacts.

Item Type: Article
Erschienen: 2014
Creators: Vu, Long and Nguyen, Phuong and Nahrstedt, Klara and Richerzhagen, Björn
Title: Characterizing and modeling people movement from mobile phone sensing traces
Language: English
Abstract:

With the ubiquity of mobile phones, a high accuracy of characterizing and modeling people movement is achievable. The knowledge about people's mobility enables many applications including highly efficient planning of cities resources and network infrastructures, or dissemination of safety alerts. However, characterizing and modeling people movement remain very challenging due to difficulties in (a) capturing, cleaning, analyzing and storing real traces, and (b) achieving accurate predictions of different future contexts.

In this paper, we present our effort in measuring and capturing phone sensory data as real traces, cleaning up measurements, and constructing prediction models. Specifically, we discuss design methodology, learned lessons from the implementation and deployment of a large-scale scanning system on 123 Google Android phones for 6 months at University of Illinois campus. We also conduct a characterization study on collected traces and present new findings in location visit pattern, location popularity, and contact pattern. Finally, we exploit joint location/contact traces to derive: (1) predictive models of missing contacts, and (2) prediction framework that provides future contextual information of people movement including locations, stay duration, and social contacts.

Journal or Publication Title: Elsevier Pervasive and Mobile Computing Journal
Publisher: ScienceDirect
Divisions: Zentrale Einrichtungen
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms > Subproject C2: Information-centred perspective
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio)
Date Deposited: 05 Feb 2015 10:44
Official URL: http://www.sciencedirect.com/science/article/pii/S1574119214...
Identification Number: MAKI-2014-51
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