ORBi: Yang Banghua - A Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models

Reference : A Data Imputation Method with Support Vector Machines for Activity-Based Transportation ...


	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Business & economic sciences : Special economic topics (health, labor, transportation…) Engineering, computing & technology : Civil engineering
	To cite this reference:	http://hdl.handle.net/2268/134331

Title :	A Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models
Language :	English
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 > > > >]
Publication date :	2011
Main document 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
Collection and collection volume :	Advances in Intelligent and Soft Computing; 122
Pages :	249-258
On invitation :	No
Audience :	International
ISBN :	978-3-642-25663-9
City :	Berlin
Country :	Germany
Event name :	Sixth International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
Event date :	15-12-2011 to 17-12-2011
Event place (city) :	Shanghai
Event country :	China
Keywords :	[en] 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.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/2268/134331
DOI :	10.1007/978-3-642-25664-6_29

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