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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13314

Title: A Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models
Authors: YANG, Banghua
Ruan, Da
COOLS, Mario
WETS, Geert
Issue Date: 2012
Publisher: Springer-Verlag Berlin Heidelberg
Citation: Wang, Yinglin & Li, Tianrui (Ed.) Advances in Intelligent and Soft Computing, 122 (2012), p. 249-257
Series/Report: Advances in Intelligent and Soft Computing
Abstract: 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.
URI: http://hdl.handle.net/1942/13314
Link to publication: http://orbi.ulg.ac.be/handle/2268/134331
DOI: 10.1007/978-3-642-25664-6_29
ISI #: 000310937600033
ISBN: 9783642256639
ISSN: 1867-5662
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2013
Appears in Collections: Research publications

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