Feature Selection Issues in Long-Term Travel Time Prediction

  • Syed Murtaza Hassan
  • Luis Moreira-Matias
  • Jihed KhiariEmail author
  • Oded Cats
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)


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.


Travel time prediction Machine learning Regression Feature selection 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Syed Murtaza Hassan
    • 1
  • Luis Moreira-Matias
    • 1
  • Jihed Khiari
    • 1
    Email author
  • Oded Cats
    • 2
  1. 1.NEC Laboratories EuropeHeidelbergGermany
  2. 2.Department of Transport and PlanningTU DelftDelftNetherlands

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