Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/15121
Title: Annotating mobile phone location data with activity purposes using machine learning algorithms
Authors: LIU, Feng 
JANSSENS, Davy 
WETS, Geert 
COOLS, Mario 
Issue Date: 2013
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Source: EXPERT SYSTEMS WITH APPLICATIONS, 40 (8), p. 3299-3311
Abstract: 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. (c) 2013 Elsevier Ltd. All rights reserved.
Notes: [Liu, Feng; Janssens, Davy; Wets, Geert] Hasselt Univ, Transportat Res Inst IMOB, B-3590 Diepenbeek, Belgium. TLU C, B-4000 Liege, Belgium.
Keywords: Activity-travel behavior; Sequential information; Machine learning algorithms; Feature selection techniques; Mobile phone location annotation;Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science
Document URI: http://hdl.handle.net/1942/15121
ISSN: 0957-4174
e-ISSN: 1873-6793
DOI: 10.1016/j.eswa.2012.12.100
ISI #: 000316522900047
Category: A1
Type: Journal Contribution
Validations: ecoom 2014
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
liu 1.pdf
  Restricted Access
article605.35 kBAdobe PDFView/Open    Request a copy
Show full item record

SCOPUSTM   
Citations

47
checked on Sep 3, 2020

WEB OF SCIENCETM
Citations

53
checked on May 21, 2022

Page view(s)

60
checked on May 23, 2022

Download(s)

54
checked on May 23, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.