Robustness Tests for Public Transport Planning

Authors Markus Friedrich, Matthias Müller-Hannemann, Ralf Rückert, Alexander Schiewe, Anita Schöbel



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Markus Friedrich
Matthias Müller-Hannemann
Ralf Rückert
Alexander Schiewe
Anita Schöbel

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Markus Friedrich, Matthias Müller-Hannemann, Ralf Rückert, Alexander Schiewe, and Anita Schöbel. Robustness Tests for Public Transport Planning. In 17th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2017). Open Access Series in Informatics (OASIcs), Volume 59, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/OASIcs.ATMOS.2017.6

Abstract

The classical planning process in public transport planning focuses on the two criteria operating costs and quality for passengers. Quality mostly considers quantities like average travel time and number of transfers. Since public transport often suffers from delays caused by random disturbances, we are interested in adding a third dimension: robustness. We propose passenger-oriented robustness indicators for public transport networks and timetables. These robustness indicators are evaluated for several public transport plans which have been created for an artificial urban network with the same demand. The study shows that these indicators are suitable to measure the robustness of a line plan and a timetable. We explore different trade-offs between operating costs, quality (average travel time of passengers), and robustness against delays. Our results show that the proposed robustness indicators give reasonable results.
Keywords
  • robustness measure
  • timetabling
  • line planning
  • delays
  • passenger-orientation

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References

  1. Rodrigo Acuna-Agost, Philippe Michelon, Dominique Feillet, and Serigne Gueye. A MIP-based local search method for the railway rescheduling problem. Networks, 57(1):69-86, 2011. Google Scholar
  2. Hannah Bast, Daniel Delling, Andrew V. Goldberg, Matthias Müller-Hannemann, Thomas Pajor, Peter Sanders, Dorothea Wagner, and Renato F. Werneck. Route planning in transportation networks. In Lasse Kliemann and Peter Sanders, editors, Algorithm Engineering - Selected Results and Surveys, volume 9220 of Lecture Notes in Computer Science, pages 19-80. Springer, 2016. URL: http://dx.doi.org/10.1007/978-3-319-49487-6_2.
  3. Nikola Bešinović, Rob M.P. Goverde, Egidio Quaglietta, and Roberto Roberti. An integrated micro-macro approach to robust railway timetabling. Transportation Research Part B: Methodological, 87:14-32, 2016. Google Scholar
  4. Serafino Cicerone, Gianlorenzo D'Angelo, Gabriele Di Stefano, Daniele Frigioni, and Alfredo Navarra. Recoverable robust timetabling for single delay: Complexity and polynomial algorithms for special cases. Journal of Combinatorial Optimization, 18(3):229, Aug 2009. Google Scholar
  5. Markus Friedrich, Maximilian Hartl, Alexander Schiewe, and Anita Schöbel. Angebotsplanung im öffentlichen Verkehr - planerische und algorithmische Lösungen. In Heureka'17, 2017. Google Scholar
  6. Philine Gattermann, Jonas Harbering, and Anita Schöbel. Line pool generation. Public Transport, 9:7-32, 2017. Google Scholar
  7. Marc Goerigk, Michael Schachtebeck, and Anita Schöbel. Evaluating line concepts using travel times and robustness: Simulations with the lintim toolbox. Public Transport, 5(3):267-284, 2013. Google Scholar
  8. Marc Goerigk and Anita Schöbel. An empirical analysis of robustness concepts for timetabling. In Proceedings of ATMOS10, volume 14 of OpenAccess Series in Informatics (OASIcs), pages 100-113, Dagstuhl, Germany, 2010. Google Scholar
  9. Marc Goerigk and Anita Schöbel. Improving the modulo simplex algorithm for large-scale periodic timetabling. Computers and Operations Research, 40(5):1363-1370, 2013. Google Scholar
  10. Géraldine Heilporn, Luigi De Giovanni, and Martine Labbé. Optimization models for the single delay management problem in public transportation. European Journal of Operational Research, 189(3):762-774, 2008. Google Scholar
  11. Leo Kroon, Gábor Maróti, Mathijn Retel Helmrich, Michiel Vromans, and Rommert Dekker. Stochastic improvement of cyclic railway timetables. Transportation Research Part B: Methodological, 42(6):553-570, 2008. Google Scholar
  12. LinTim - Integrated Optimization in Public Transportation. Homepage. see URL: http://lintim.math.uni-goettingen.de/.
  13. Matthias Müller-Hannemann and Mathias Schnee. Efficient timetable information in the presence of delays. In R. Ahuja, R.-H. Möhring, and C. Zaroliagis, editors, Robust and Online Large-Scale Optimization, volume 5868 of Lecture Notes in Computer Science, pages 249-272. Springer, 2009. Google Scholar
  14. Ralf Rückert, Martin Lemnian, Christoph Blendinger, Steffen Rechner, and Matthias Müller-Hannemann. PANDA: a software tool for improved train dispatching with focus on passenger flow. Public Transportation, 2016. DOI 10.1007/s12469-016-0140-0. Google Scholar
  15. Anita Schöbel. A model for the delay management problem based on mixed-integer programming. Electronic Notes in Theoretical Computer Science, 50(1), 2001. Google Scholar
  16. Anita Schöbel. Optimization in public transportation. Stop location, delay management and tariff planning from a customer-oriented point of view. Optimization and Its Applications. Springer, New York, 2006. Google Scholar
  17. Anita Schöbel. Line planning in public transportation: models and methods. OR Spectrum, 34(3):491-510, Jul 2012. Google Scholar
  18. Anita Schöbel. An eigenmodel for iterative line planning, timetabling and vehicle scheduling in public transportation. Transportation Research Part C: Emerging Technologies, 74:348-365, 2017. Google Scholar
  19. Peter Sels, Thijs Dewilde, Dirk Cattrysse, and Pieter Vansteenwegen. Reducing the passenger travel time in practice by the automated construction of a robust railway timetable. Transportation Research Part B: Methodological, 84:124-156, 2016. Google Scholar
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