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International Conference on Database Systems for Advanced Applications

DASFAA 2014: Database Systems for Advanced Applications pp 46–60Cite as

Short-Term Speed Prediction on Urban Highways by Ensemble Learning with Feature Subset Selection

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8505))

Abstract

Accurate traffic speed prediction is essential in the development of intelligent transportation systems. Even though a lot of methods have been proposed for traffic prediction, few works pay attention to the application of ensemble learning and feature subset selection. In this paper, we propose an implementation of ensemble learning using combination of M5 model tree and bagging to tackle traffic speed prediction. A method to select optimal neighboring links as features for our prediction model is also introduced, and different feature subset selection methods are compared. Experimental results show that the proposed ensemble with feature subset selection outperforms both single model and nonparametric model (k-NN).

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Acknowledgments

This research was supported by MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-(H0301-13-1012)) supervised by the NIPA (National IT Industry Promotion Agency).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 609-735, South Korea

    Mohammad Arif Rasyidi & Kwang Ryel Ryu

Authors
  1. Mohammad Arif Rasyidi
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  2. Kwang Ryel Ryu
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Corresponding author

Correspondence to Kwang Ryel Ryu .

Editor information

Editors and Affiliations

  1. Pohang University of Science and Technology (POSTECH), Pohang, Korea, Republic of (South Korea)

    Wook-Shin Han

  2. National University of Singapore, Singapore, Singapore

    Mong Li Lee

  3. Udayana University, Badung, Indonesia

    Agus Muliantara

  4. Udayana University, Badung, Indonesia

    Ngurah Agus Sanjaya

  5. Christian-Albrechts-Universität zu Kiel Institut für Informatik, Kiel, Germany

    Bernhard Thalheim

  6. Fudan University, Shanghai, China

    Shuigeng Zhou

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Rasyidi, M.A., Ryu, K.R. (2014). Short-Term Speed Prediction on Urban Highways by Ensemble Learning with Feature Subset Selection. In: Han, WS., Lee, M., Muliantara, A., Sanjaya, N., Thalheim, B., Zhou, S. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science(), vol 8505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43984-5_4

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  • DOI: https://doi.org/10.1007/978-3-662-43984-5_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43983-8

  • Online ISBN: 978-3-662-43984-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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