Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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 19–80
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 290–301
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 42–54
Cárdenas CJ (2013) Efficient multi-modal route planning with transfer Patterns. Master’s thesis Freiburg University
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 Mathematics
Dibbelt J, Pajor T, Strasser B, Wagner D (2013) Intriguingly simple and fast transit routing. In: International symposium on experimental algorithms. Springer, pp 43–54
Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische mathematik 1(1):269–271
Gal A, Mandelbaum A, Schnitzler F, Senderovich A, Weidlich M (2015) Traveling time prediction in scheduled transportation with journey segments. Inf Syst
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 319–333
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 Informatik
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):100–107
Higgins A, Kozan E (1998) Modeling train delays in urban networks. Transp Sci 32(4):346–357
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 CEUR-WS.org, pp 331–338
Liebig T, Piatkowski N, Bockermann C, Morik K (2017) Dynamic route planning with real-time traffic predictions. Inf Syst 64:258–265
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 43–48
Müller-Hannemann M, Schnee M (2009) Efficient timetable information in the presence of delays. In: Robust and online large-scale optimization. Springer, pp 249–272
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 520–523
Zygouras N, Zacheilas N, Kalogeraki V, Kinane D, Gunopulos D (2015) Insights on a scalable and dynamic traffic management system. In: EDBT, pp 653–664
Acknowledgements
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”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liebig, T., Peter, S., Grzenda, M., Junosza-Szaniawski, K. (2017). Dynamic Transfer Patterns for Fast Multi-modal Route Planning. In: Bregt, A., Sarjakoski, T., van Lammeren, R., Rip, F. (eds) Societal Geo-innovation. AGILE 2017. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-56759-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-56759-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56758-7
Online ISBN: 978-3-319-56759-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)