Dynamic Transfer Patterns for Fast Multi-modal Route Planning

  • Thomas Liebig
  • Sebastian Peter
  • Maciej Grzenda
  • Konstanty Junosza-Szaniawski
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Route planning makes direct use of geographic data and provides beneficial recommendations to the public. In real-world the schedule of transit vehicles is dynamic and delays in the schedules occur. Incorporation of these dynamic schedule changes in multi-modal route computation is difficult and requires a lot of computational resources. Our approach extends the state-of-the-art for static transit schedules, Transfer Patterns, for the dynamic case. Therefore, we amend the patterns by additional edges that cover the dynamics. Our approach is implemented in the open-source routing framework OpenTripPlanner and compared to existing methods in the city of Warsaw. Our results are an order of magnitude faster then existing methods.


Public Transport Transit Network Route Planning Transfer Pattern Dynamic Transit 
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.



The authors received funding from the European Union Horizon 2020 Programme (Horizon2020/2014–2020), under grant agreement number 688380 “VaVeL: Variety, Veracity, VaLue: Handling the Multiplicity of Urban Sensors”.


  1. Bast H, Delling D, Goldberg A, Müller-Hannemann M, Pajor T, Sanders P, Wagner D, Werneck RF (2016) Route planning in transportation networks. Springer International Publishing, Cham, pp 19–80Google Scholar
  2. Bast H, Carlsson E, Eigenwillig A, Geisberger R, Harrelson C, Raychev V, Viger F (2010) Fast routing in very large public transportation networks using transfer patterns. In: European symposium on algorithms. Springer, pp 290–301Google Scholar
  3. Bast H, Sternisko J, Storandt S (2013) Delay-robustness of transfer patterns in public transportation route planning. In: ATMOS-13th workshop on algorithmic approaches for transportation modelling, optimization, and systems-2013, vol 33 Schloss Dagstuhl Leibniz-Zentrum fuer Informatik pp 42–54Google Scholar
  4. Cárdenas CJ (2013) Efficient multi-modal route planning with transfer Patterns. Master’s thesis Freiburg UniversityGoogle Scholar
  5. Delling D, Pajor T, Werneck R (2012) Round-based public transit routing. In: Proceedings of the 14th meeting on algorithm engineering and experiments (ALENEX’12). Society for Industrial and Applied MathematicsGoogle Scholar
  6. Dibbelt J, Pajor T, Strasser B, Wagner D (2013) Intriguingly simple and fast transit routing. In: International symposium on experimental algorithms. Springer, pp 43–54Google Scholar
  7. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische mathematik 1(1):269–271CrossRefGoogle Scholar
  8. Gal A, Mandelbaum A, Schnitzler F, Senderovich A, Weidlich M (2015) Traveling time prediction in scheduled transportation with journey segments. Inf SystGoogle Scholar
  9. Geisberger R, Sanders P, Schultes D, Delling D (2008) Contraction hierarchies: faster and simpler hierarchical routing in road networks. In: International workshop on experimental and efficient algorithms. Springer, pp 319–333Google Scholar
  10. Goerigk M, Knoth M, Müller-Hannemann M, Schmidt M, Schöbel A (2011) The price of robustness in timetable information. In: OASIcs-openaccess series in informatics. Volume 20 Schloss Dagstuhl-Leibniz-Zentrum für InformatikGoogle Scholar
  11. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107CrossRefGoogle Scholar
  12. Higgins A, Kozan E (1998) Modeling train delays in urban networks. Transp Sci 32(4):346–357CrossRefGoogle Scholar
  13. Liebig T, Piatkowski N, Bockermann C, Morik K (2014) Predictive trip planning—smart routing in smart cities. In: Proceedings of the workshops of the EDBT/ICDT 2014 joint conference (EDBT/ICDT 2014), Athens, Greece, March 28, 2014. Volume 1133, pp 331–338Google Scholar
  14. Liebig T, Piatkowski N, Bockermann C, Morik K (2017) Dynamic route planning with real-time traffic predictions. Inf Syst 64:258–265CrossRefGoogle Scholar
  15. Mazimpaka JD, Timpf S (2016) A visual and computational analysis approach for exploring significant locations and time periods along a bus route. In: Proceedings of the 9th ACM SIGSPATIAL international workshop on computational transportation science. ACM, pp 43–48Google Scholar
  16. Müller-Hannemann M, Schnee M (2009) Efficient timetable information in the presence of delays. In: Robust and online large-scale optimization. Springer, pp 249–272Google Scholar
  17. Schnitzler F, Artikis A, Weidlich M, Boutsis I, Liebig T, Piatkowski N, Bockermann C, Morik K, Kalogeraki V, Marecek J, Gal A, Mannor S, Kinane D, Gunopulos D (2014) Heterogeneous stream processing and crowdsourcing for traffic monitoring: highlights. In: Machine learning and knowledge discovery in databases. Volume 8726 of Lecture notes in computer science. Springer, Berlin, pp 520–523Google Scholar
  18. Zygouras N, Zacheilas N, Kalogeraki V, Kinane D, Gunopulos D (2015) Insights on a scalable and dynamic traffic management system. In: EDBT, pp 653–664Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Thomas Liebig
    • 1
  • Sebastian Peter
    • 1
  • Maciej Grzenda
    • 2
  • Konstanty Junosza-Szaniawski
    • 2
  1. 1.TU Dortmund UniversityDortmundGermany
  2. 2.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland

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