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Visual Analytics for Supporting Conflict Resolution in Large Railway Networks

  • Udo SchlegelEmail author
  • Wolfgang Jentner
  • Juri Buchmueller
  • Eren Cakmak
  • Giuliano Castiglia
  • Renzo Canepa
  • Simone Petralli
  • Luca Oneto
  • Daniel A. Keim
  • Davide Anguita
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)

Abstract

Train operators are responsible for maintaining and following the schedule of large-scale railway transport systems. Disruptions to this schedule imply conflicts that occur when two trains are bound to use the same railway segment. It is upon the train operator to decide which train must go first to resolve the conflict. As the railway transport system is a large and complex network, the decision may have a high impact on the future schedule, further train delay, costs, and other performance indicators. Due to this complexity and the enormous amount of underlying data, machine learning models have proven to be useful. However, the automated models are not accessible to the train operators which results in a low trust in following their predictions. We propose a Visual Analytics solution for a decision support system to support the train operators in making an informed decision while providing access to the complex machine learning models. Different integrated, interactive views allow the train operator to explore the various impacts that a decision may have. Additionally, the user can compare various data-driven models which are structured by an experience-based model. We demonstrate a decision-making process in a use case highlighting how the different views are made use of by the train operator.

Keywords

Visual and big data analytics Decision support systems 

Notes

Acknowledgments

This research has been supported by the European Union through the project IN2DREAMS (European Union’s Horizon 2020 research and innovation programme under grant agreement 777596).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Udo Schlegel
    • 1
    Email author
  • Wolfgang Jentner
    • 1
  • Juri Buchmueller
    • 1
  • Eren Cakmak
    • 1
  • Giuliano Castiglia
    • 1
  • Renzo Canepa
    • 2
  • Simone Petralli
    • 2
  • Luca Oneto
    • 3
  • Daniel A. Keim
    • 1
  • Davide Anguita
    • 3
  1. 1.DBVISUniversity of KonstanzKonstanzGermany
  2. 2.Rete Ferroviaria Italiana S.p.A.RomeItaly
  3. 3.DIBRISUniversity of GenoaGenoaItaly

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