Robust Routing in Urban Public Transportation: Evaluating Strategies that Learn From the Past

Authors Katerina Böhmová, Matúš Mihalák, Peggy Neubert, Tobias Pröger, Peter Widmayer

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Katerina Böhmová
Matúš Mihalák
Peggy Neubert
Tobias Pröger
Peter Widmayer

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Katerina Böhmová, Matúš Mihalák, Peggy Neubert, Tobias Pröger, and Peter Widmayer. Robust Routing in Urban Public Transportation: Evaluating Strategies that Learn From the Past. In 15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2015). Open Access Series in Informatics (OASIcs), Volume 48, pp. 68-81, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)


Given an urban public transportation network and historic delay information, we consider the problem of computing reliable journeys. We propose new algorithms based on our recently presented solution concept (Böhmová et al., ATMOS 2013), and perform an experimental evaluation using real-world delay data from Zürich, Switzerland. We compare these methods to natural approaches as well as to our recently proposed method which can also be used to measure typicality of past observations. Moreover, we demonstrate how this measure relates to the predictive quality of the individual methods. In particular, if the past observations are typical, then the learning- based methods are able to produce solutions that perform well on typical days, even in the presence of large delays.
  • public transportation
  • route planning
  • robustness
  • optimization
  • experiments


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