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http://hdl.handle.net/1942/13314
Title: | A Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models | Authors: | YANG, Banghua JANSSENS, Davy RUAN, Da COOLS, Mario BELLEMANS, Tom WETS, Geert |
Issue Date: | 2012 | Publisher: | Springer-Verlag Berlin Heidelberg | Source: | 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. | Keywords: | activity-based transportation models; support vector machine (SVM); data imputation; missing data | Document URI: | http://hdl.handle.net/1942/13314 | Link to publication/dataset: | http://orbi.ulg.ac.be/handle/2268/134331 | ISBN: | 9783642256639 | DOI: | 10.1007/978-3-642-25664-6_29 | ISI #: | 000310937600033 | Rights: | © Springer-Verlag Berlin Heidelberg 2011. | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2013 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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springerA Data Imputation Method.pdf | Peer-reviewed author version | 192.82 kB | Adobe PDF | View/Open |
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