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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Moons, E., Wets, G.: Tackling Non-response in Household Travel Surveys: A Case Study. In: Proceeding of the 56th Session of the International Statistics Institute, Lisbon, CD-ROM (2007)
Janssens, D., Wets, G., Timmemans, H.J.P.: Modeling Short-term Dynamics in Activity-Travel Patterns: the Feathers Model. In: Proceedings of the 11th World Conference on Transportation Research. WCTRS, Berkeley, California, USA, CD-ROM (2007)
Bellemans, T., Kochan, B., Janssens, D., Wets, G.: Implementation Framework and Development Trajectory of FEATHERS Activity-based Simulation Platform. Transportation Research Record: Journal of the Transportation Research Board, No. 2175, Transportation Research Board of the National Academies, Washington, D.C., 111–119 (2010)
Cools, M., Moons, E., Bellemans, T., Janssens, D., Wets, G.: Surveying Activity-travel Behavior in Flanders: Assessing the Impact of the Survey Design. In: Proceedings of the BIVEC-GIBET Transport Research Day, Part II, Brussels, pp. 727–741 (2009)
Nakamya, J., Moons, E., Koelet, S., Wets, G.: Impact of Data Iintegration on Some Iimportant Travel Behavior Indicators. Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C, 89–94 (2007)
Arentze, T., Hofman, F., Nelly Kalfs, N., Immermans, H.: System for Logical Verification and Inference of Activity (SYLVIA) Diaries. Transportation Research Record 1660, 156–163 (1999)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer (1995)
Cortes, C., Vapnik, V.: Support-vector Networks. Machine Learning 20, 273–297 (1995)
Hearst, M.A., Schölkopf, B., Dumais, S., Osuna, E.: Trends and Controversies - Support Vector Machines. IEEE Intelligent Systems 13, 18–28 (1998)
Sanchez, V.D.: Advanced Support Vector Machines and Kernel Methods. Neuro Computing 55, 5–20 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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: Wang, Y., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25664-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-25664-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25663-9
Online ISBN: 978-3-642-25664-6
eBook Packages: EngineeringEngineering (R0)