Article (Scientific journals)
Annotating mobile phone location data with activity purposes using machine learning algorithms
Liu, Feng; Janssens, Davy; Wets, Geert et al.
2013In Expert Systems with Applications, 40 (8), p. 3299–3311
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Keywords :
Activity-travel behavior; Sequential information; Machine learning algorithms; Feature selection techniques; Mobile phone location annotation
Abstract :
[en] Individual human travel patterns captured by mobile phone data have been quantitatively characterized by mathematical models, but the underlying activities which initiate the movement are still in a less-explored stage. As a result of the nature of how activity and related travel decisions are made in daily life, human activity-travel behavior exhibits a high degree of spatial and temporal regularities as well as sequential ordering. In this study, we investigate to what extent the behavioral routines could reveal the activities being performed at mobile phone call locations that are captured when users initiate or receive a voice call or message. Our exploration consists of four steps. First, we define a set of comprehensive temporal variables characterizing each call location. Feature selection techniques are then applied to choose the most effective variables in the second step. Next, a set of state-of-the-art machine learning algorithms including Support Vector Machines, Logistic Regression, Decision Trees and Random Forests are employed to build classification models. Alongside, an ensemble of the results of the above models is also tested. Finally, the inference performance is further enhanced by a post-processing algorithm. Using data collected from natural mobile phone communication patterns of 80 users over a period of more than one year, we evaluated our approach via a set of extensive experiments. Based on the ensemble of the models, we achieved prediction accuracy of 69.7%. Furthermore, using the post processing algorithm, the performance obtained a 7.6% improvement. The experiment results demonstrate the potential to annotate mobile phone locations based on the integration of data mining techniques with the characteristics of underlying activity-travel behavior, contributing towards the semantic comprehension and further application of the massive data.
Research center :
Lepur : Centre de Recherche sur la Ville, le Territoire et le Milieu rural - ULiège
LEMA - Local Environment Management and Analysis
Disciplines :
Special economic topics (health, labor, transportation...)
Civil engineering
Author, co-author :
Liu, Feng;  Universiteit Hasselt - UH
Janssens, Davy;  Universiteit Hasselt - UH
Wets, Geert;  Universiteit Hasselt - UH
Cools, Mario  ;  Université de Liège - ULiège > Département Argenco : Secteur TLU+C > Transports et mobilité
Language :
English
Title :
Annotating mobile phone location data with activity purposes using machine learning algorithms
Publication date :
2013
Journal title :
Expert Systems with Applications
ISSN :
0957-4174
eISSN :
1873-6793
Publisher :
Pergamon Press - An Imprint of Elsevier Science
Volume :
40
Issue :
8
Pages :
3299–3311
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 18 February 2013

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