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Feature Selection Issues in Long-Term Travel Time Prediction

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Advances in Intelligent Data Analysis XV (IDA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9897))

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Abstract

Long-term travel time predictions are crucial for tactical and operational public transport planning in schedule design and resource allocation tasks. Similarly to any regression task, its success considerably depend on an adequate feature selection framework. In this paper, we approach the myopia of the State-of-the-Art method RReliefF on mining relevant inter-relationships of the feature space relevant for reducing the entropy around the target variable on regression tasks. A comparative study was conducted using baseline regression methods and LASSO as a valid alternative to RReliefF. Experimental results obtained on a real-world case study uncovered the bias/variance reduction obtained by each approach, pointing out promising ideas on this research line.

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Author information

Authors and Affiliations

  1. NEC Laboratories Europe, 69115, Heidelberg, Germany

    Syed Murtaza Hassan, Luis Moreira-Matias & Jihed Khiari

  2. Department of Transport and Planning, TU Delft, 2600, Delft, Netherlands

    Oded Cats

Authors
  1. Syed Murtaza Hassan
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  2. Luis Moreira-Matias
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  3. Jihed Khiari
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  4. Oded Cats
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Corresponding author

Correspondence to Jihed Khiari .

Editor information

Editors and Affiliations

  1. Stockholm University , Stockholm, Sweden

    Henrik Boström

  2. Leiden University , Leiden, The Netherlands

    Arno Knobbe

  3. University of Porto , Porto, Portugal

    Carlos Soares

  4. Stockholm University , Stockholm, Sweden

    Panagiotis Papapetrou

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Hassan, S.M., Moreira-Matias, L., Khiari, J., Cats, O. (2016). Feature Selection Issues in Long-Term Travel Time Prediction. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-46349-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46348-3

  • Online ISBN: 978-3-319-46349-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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