A data imputation method with support vector machines for activity-based transportation models

Banghua Yang; Davy Janssens; Da Ruan; Mario Cools; Tom Bellemans; Geert Wets

doi:10.1007/978-3-642-25664-6_29

A data imputation method with support vector machines for activity-based transportation models

Banghua Yang, Davy Janssens, Da Ruan, Mario Cools, Tom Bellemans, Geert Wets

Research output › peer-review

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.

Original language	English
Title of host publication	Foundations of Intelligent Systems
Subtitle of host publication	Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011)
Editors	Yinglin Wang, Tianrui Li
Pages	249-257
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-25664-6_29
State	Published - 2011

Publication series

Name	Advances in Intelligent and Soft Computing
Volume	122
ISSN (Print)	1867-5662

ASJC Scopus subject areas

General Computer Science

Access to Document

10.1007/978-3-642-25664-6_29License: Unspecified

Cite this

Yang, B., Janssens, D., Ruan, D., Cools, M., Bellemans, T., & Wets, G. (2011). A data imputation method with support vector machines for activity-based transportation models. In Y. Wang, & T. Li (Eds.), Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011) (pp. 249-257). (Advances in Intelligent and Soft Computing; Vol. 122). https://doi.org/10.1007/978-3-642-25664-6_29

Yang, Banghua ; Janssens, Davy ; Ruan, Da et al. / A data imputation method with support vector machines for activity-based transportation models. Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011). editor / Yinglin Wang ; Tianrui Li. 2011. pp. 249-257 (Advances in Intelligent and Soft Computing).

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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, Data imputation, Missing data, Support Vector Machine (SVM)",

author = "Banghua Yang and Davy Janssens and Da Ruan and Mario Cools and Tom Bellemans and Geert Wets",

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Yang, B, Janssens, D, Ruan, D, Cools, M, Bellemans, T & Wets, G 2011, A data imputation method with support vector machines for activity-based transportation models. in Y Wang & T Li (eds), Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011). Advances in Intelligent and Soft Computing, vol. 122, pp. 249-257. https://doi.org/10.1007/978-3-642-25664-6_29

A data imputation method with support vector machines for activity-based transportation models. / Yang, Banghua; Janssens, Davy; Ruan, Da et al.
Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011). ed. / Yinglin Wang; Tianrui Li. 2011. p. 249-257 (Advances in Intelligent and Soft Computing; Vol. 122).

Research output › peer-review

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Yang B, Janssens D, Ruan D, Cools M, Bellemans T, Wets G. A data imputation method with support vector machines for activity-based transportation models. In Wang Y, Li T, editors, Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011). 2011. p. 249-257. (Advances in Intelligent and Soft Computing). doi: 10.1007/978-3-642-25664-6_29