TY - JOUR
T1 - Characterizing and modeling people movement from mobile phone sensing traces
AU - Vu, Long
AU - Nguyen, Phuong
AU - Nahrstedt, Klara
AU - Richerzhagen, Björn
N1 - Funding Information:
This research was supported by the National Foundation Grant NSF CISE CNS 1346782 , the German Research Society (DFG) SFB 1053 MAKI Grant, and by the Deutsche Telecom Gift Grant. All results, findings and claims belong to the authors and do not reflect the opinions of the funding organizations.
PY - 2015/2/1
Y1 - 2015/2/1
N2 - 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.
AB - 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.
KW - Bluetooth trace
KW - People movement prediction
KW - WiFi trace
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U2 - 10.1016/j.pmcj.2014.12.001
DO - 10.1016/j.pmcj.2014.12.001
M3 - Article
AN - SCOPUS:84924237677
VL - 17
SP - 220
EP - 235
JO - Pervasive and Mobile Computing
JF - Pervasive and Mobile Computing
SN - 1574-1192
IS - PB
ER -