Advertisement

An assignment model for public transport networks with both schedule- and frequency-based services

  • Morten EltvedEmail author
  • Otto Anker Nielsen
  • Thomas Kjær Rasmussen
Research Paper
  • 57 Downloads

Abstract

This paper presents an assignment modeling framework for public transport networks with co-existing schedule- and frequency-based services. The paper develops, applies and discusses a joint model, which aims at representing the behavior of passengers as realistically as possible. The model consists of a choice set generation phase followed by a multinomial logit route choice model and assignment of flow to the generated alternatives. The choice set generation uses an event dominance principle to exclude alternatives with costs above a certain cost threshold. Furthermore, a heuristic for aggregating overlapping lines is proposed. The results from applying the model to a case study in the Greater Copenhagen Area show that the level of service obtained in the unified network model of mixed services is placed between the level of service for strictly schedule-based and strictly frequency-based networks. The results also show that providing timetable information to the passengers improve their utility function as compared to only providing information on frequencies.

Keywords

Public transport Route choice Path-based assignment Frequency based Schedule based Event dominance Static versus dynamic 

Notes

References

  1. Anderson MK, Nielsen OA, Prato CG (2014) Multimodal route choice models of public transport passengers in the Greater Copenhagen Area. EURO J Transp Logist 6(3):221–245.  https://doi.org/10.1007/s13676-014-0063-3 CrossRefGoogle Scholar
  2. Cascetta E, Coppola P (2016) Assessment of schedule-based and frequency-based assignment models for strategic and operational planning of high-speed rail services. Transp Res Part A 84:93–108.  https://doi.org/10.1016/j.tra.2015.09.010 CrossRefGoogle Scholar
  3. de Cea J, Fernández E (1993) Transit assignment for congested public transport systems: an equilibrium model. Transp Sci 27(2):133–147.  https://doi.org/10.1287/trsc.27.2.133 CrossRefGoogle Scholar
  4. Chriqui C, Robillard P (1975) Common bus lines. Transp Sci 9(2):115–121CrossRefGoogle Scholar
  5. Florian M (1999) Deterministic time table transit assignment. In: First Asian EMME/2 Users Group Meeting, Shanghai, p 15Google Scholar
  6. Florian M (2004) Finding shortest time-dependent paths in schedule-based transit networks: a label setting algorithm. In: Wilson N, Nuzzolo A (eds) Schedule-based dynamic transit modeling: theory and applications. Kluwer Academic Publishers, Dordrecht, pp 43–52CrossRefGoogle Scholar
  7. Friedrich M, Hofsaess I, Wekeck S (2001) Timetable-based transit assignment using branch and bound techniques. Transp Res Rec 1752:100–107CrossRefGoogle Scholar
  8. Gentile G, Noekel K (2016) Modelling public transport passenger flows in the era of intelligent transport systems, vol 1. Springer, Berlin.  https://doi.org/10.1007/978-3-319-25082-3 CrossRefGoogle Scholar
  9. Hoogendoorn-Lanser S, Bovy P, Van Nes R (2007) Application of constrained enumeration approach to multimodal choice set generation. Transp Res Rec J Transp Res Board 2014:50–57.  https://doi.org/10.3141/2014-07 CrossRefGoogle Scholar
  10. Ingvardson JB, Nielsen OA, Raveau S, Nielsen BF (2018) Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: a smart card data analysis. Transp Res Part C 90:292–306.  https://doi.org/10.1016/j.trc.2018.03.006 CrossRefGoogle Scholar
  11. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263.  https://doi.org/10.2307/1914185 CrossRefGoogle Scholar
  12. Liu Y, Bunker J, Ferreira L (2010) Transit users’ route-choice modelling in transit assignment: a review. Transp Rev 30(6):753–769.  https://doi.org/10.1080/01441641003744261 CrossRefGoogle Scholar
  13. Nielsen OA (2000) A stochastic transit assignment model considering differences in passengers utility functions. Transp Res Part B 34(5):377–402CrossRefGoogle Scholar
  14. Nielsen OA, Frederiksen RD (2006) Optimisation of timetable-based, stochastic transit assignment models based on MSA. Ann Oper Res 144(1):263–285.  https://doi.org/10.1007/s10479-006-0012-0 CrossRefGoogle Scholar
  15. Nökel K, Wekeck S (2007) Choice models in frequency-based transit assignment. In: Proceedings of the European transport conference (ETC), Noordwijkerhout, NetherlandsGoogle Scholar
  16. Olds EG (1952) A note on the convolution of uniform distributions. Ann Math Stat 23(2):282–285CrossRefGoogle Scholar
  17. Prato CG (2009) Route choice modeling: past, present and future research directions. J Choice Model 2(1):65–100.  https://doi.org/10.1016/S1755-5345(13)70005-8 CrossRefGoogle Scholar
  18. Schmöcker JD, Shimamoto H, Kurauchi F (2013) Generation and calibration of transit hyperpaths. Transp Res Part C 36:406–418.  https://doi.org/10.1016/j.trc.2013.06.014 CrossRefGoogle Scholar
  19. Train KE (2002) Discrete choice methods with simulation. Cambridge University Press, CambridgeGoogle Scholar
  20. Watling DP, Rasmussen TK, Prato CG, Nielsen OA (2018) Stochastic user equilibrium with a bounded choice model. Transp Res Part B 114:254–280.  https://doi.org/10.1016/j.trb.2018.05.004 CrossRefGoogle Scholar
  21. Wilson N, Nuzzolo A (eds) (2009) Schedule-based modeling of transportation networks: theory and applications. Springer, Berlin.  https://doi.org/10.1007/978-0-387-84811-2 CrossRefGoogle Scholar

Copyright information

© The Association of European Operational Research Societies and Springer-Verlag GmbH Berlin Heidelberg 2019

Authors and Affiliations

  1. 1.Technical University of DenmarkKongens LyngbyDenmark
  2. 2.Technical University of DenmarkKongens LyngbyDenmark
  3. 3.Technical University of DenmarkKongens LyngbyDenmark

Personalised recommendations