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Understanding the Effectiveness of Data Reduction in Public Transportation Networks

  • Thomas Bläsius
  • Philipp FischbeckEmail author
  • Tobias Friedrich
  • Martin Schirneck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11631)

Abstract

Given a public transportation network of stations and connections, we want to find a minimum subset of stations such that each connection runs through a selected station. Although this problem is NP-hard in general, real-world instances are regularly solved almost completely by a set of simple reduction rules. To explain this behavior, we view transportation networks as hitting set instances and identify two characteristic properties, locality and heterogeneity. We then devise a randomized model to generate hitting set instances with adjustable properties. While the heterogeneity does influence the effectiveness of the reduction rules, the generated instances show that locality is the significant factor. Beyond that, we prove that the effectiveness of the reduction rules is independent of the underlying graph structure. Finally, we show that high locality is also prevalent in instances from other domains, facilitating a fast computation of minimum hitting sets.

Keywords

Transportation networks Hitting set Graph algorithms Random graph models 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Thomas Bläsius
    • 1
  • Philipp Fischbeck
    • 1
    Email author
  • Tobias Friedrich
    • 1
  • Martin Schirneck
    • 1
  1. 1.Hasso Plattner InstituteUniversity of PotsdamPotsdamGermany

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