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A Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models
Yang, Banghua; Janssens, Davy; Ruan, Da et al.
2011In Wang, Y.; Li, T. (Eds.) Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE 2011)
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Keywords :
Activity-based transportation models; Support Vector Machine (SVM); Data imputation; Missing data
Abstract :
[en] In this paper, a data imputation method with a Support Vector Machine (SVM) is proposed to solve the issue of missing data in activity-based diaries. Here two SVM models are established to predict the missing elements of ‘number of cars’ and ‘driver license’. The inputs of the former SVM model include five variables (Household composition, household income, Age oldest household member, Children age class and Number of household members). The inputs of the latter SVM model include three variables (personal age, work status and gender). The SVM models to predict the ‘number of cars’ and ‘driver license’ can achieve accuracies of 69% and 83% respectively. The initial experimental results show that missing elements of observed activity diaries can be accurately inferred by relating different pieces of information. Therefore, the proposed SVM data imputation method serves as an effective data imputation method in the case of missing information.
Research center :
Lepur : Centre de Recherche sur la Ville, le Territoire et le Milieu rural - ULiège
LEMA - Local Environment Management and Analysis
Disciplines :
Civil engineering
Special economic topics (health, labor, transportation...)
Author, co-author :
Yang, Banghua;  Universiteit Hasselt - UH
Janssens, Davy;  Universiteit Hasselt - UH
Ruan, Da;  Universiteit Hasselt - UH
Cools, Mario  ;  Universiteit Hasselt - UH
Bellemans, Tom;  Universiteit Hasselt - UH
Wets, Geert;  Universiteit Hasselt - UH
Language :
English
Title :
A Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models
Publication date :
2011
Event name :
Sixth International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
Event place :
Shanghai, China
Event date :
15-12-2011 to 17-12-2011
Audience :
International
Main work title :
Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE 2011)
Editor :
Wang, Y.
Li, T.
Publisher :
Springer-Verlag, Berlin, Germany
ISBN/EAN :
978-3-642-25663-9
Collection name :
Advances in Intelligent and Soft Computing; 122
Pages :
249-258
Peer reviewed :
Peer reviewed
Available on ORBi :
since 16 November 2012

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