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Public Transit Labeling

  • Daniel Delling
  • Julian Dibbelt
  • Thomas Pajor
  • Renato F. Werneck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9125)

Abstract

We study the journey planning problem in public transit networks. Developing efficient preprocessing-based speedup techniques for this problem has been challenging: current approaches either require massive preprocessing effort or provide limited speedups. Leveraging recent advances in Hub Labeling, the fastest algorithm for road networks, we revisit the well-known time-expanded model for public transit. Exploiting domain-specific properties, we provide simple and efficient algorithms for the earliest arrival, profile, and multicriteria problems, with queries that are orders of magnitude faster than the state of the art.

Keywords

Public Transit Early Arrival Arrival Event Event Label Distance Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daniel Delling
    • 1
  • Julian Dibbelt
    • 2
  • Thomas Pajor
    • 3
  • Renato F. Werneck
    • 4
  1. 1.SunnyvaleUSA
  2. 2.Karlsruhe Institute of TechnologyKarlsruheGermany
  3. 3.Microsoft ResearchNew YorkUSA
  4. 4.San FranciscoUSA

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