Incorporating Stability of Mode Choice into an Agent-Based Travel Demand Model

  • Nicolai MalligEmail author
  • Peter Vortisch
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 722)


Agent-based modelling is a promising technique, which allows to combine the advantages of different approaches to travel demand modelling. Agent-based modelling provides a framework that allows to easily substitute individual submodels. This paper shows, using the example of mobiTopp, how stability of mode choice can be integrated into an agent-based travel demand model. This has been achieved by replacing the submodel for mode choice by an extended variant, which takes stability of mode choice into account. The improved model reproduces this stability, as measured by two indicators based on mode usage and mobility styles, quite well.


Mode Choice Travel Behaviour Travel Demand Multinomial Logit Model Mode Usage 
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.



This work has been supported by Deutsche Forschungsgemeinschaft (DFG) under grant No. VO 1791/4-1.


  1. 1.
    Adamowicz, W.L.: Habit formation and variety seeking in a discrete choice model of recreation demand. J. Agric. Resour. Econ. 19(1), 19–31 (1994)Google Scholar
  2. 2.
    Arentze, T.A., Ettema, D., Timmermans, H.J.: Location choice in the context of multi-day activity-travel patterns: model development and empirical results. Transp. A: Transp. Sci. 9(2), 107–123 (2013)Google Scholar
  3. 3.
    Beckman, R.J., Baggerly, K.A., McKay, M.D.: Creating synthetic baseline populations. Transp. Res. Part A: Policy Pract. 30(6), 415–429 (1996)Google Scholar
  4. 4.
    Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Nat. Acad. Sci. U.S.A. 99(10, Suppl. 3), 7280–7287 (2002)CrossRefGoogle Scholar
  5. 5.
    Bowman, J.L., Ben-Akiva, M.E.: Activity-based disaggregate travel demand model system with activity schedules. Transp. Res. Part A: Policy Pract. 35(1), 1–28 (2001)CrossRefGoogle Scholar
  6. 6.
    Cirillo, C., Axhausen, K.W.: Dynamic model of activity-type choice and scheduling. Transportation 37(1), 15–38 (2010)CrossRefGoogle Scholar
  7. 7.
    Hägerstraand, T.: What about people in regional science? Pap. Reg. Sci. 24(1), 7–24 (1970)CrossRefGoogle Scholar
  8. 8.
    Heilig, M., Mallig, N., Schröder, O., Kagerbauer, M., Vortisch, P.: Implementation of free-floating and station-based carsharing in an agent-based travel demand model. Travel Behav. Soc. (2017). doi:
  9. 9.
    Henson, K., Goulias, K., Golledge, R.: An assessment of activity-based modeling and simulation for applications in operational studies, disaster preparedness, and homeland security. Transp. Lett. 1(1), 19–39 (2009)CrossRefGoogle Scholar
  10. 10.
    Hilgert, T., Heilig, M., Kagerbauer, M., Vortisch, P.: Modeling week activity schedules for travel demand models. In: 96th Annual Meeting of the Transportation Research Board. Washington, DC, January 2017Google Scholar
  11. 11.
    Mallig, N., Heilig, M., Weiss, C., Chlond, B., Vortisch, P.: Modelling the weekly electricity demand caused by electric cars. Future Gen. Comput. Syst. 64, 140–150 (2016)CrossRefGoogle Scholar
  12. 12.
    Mallig, N., Kagerbauer, M., Vortisch, P.: mobiTopp - a modular agent-based travel demand modelling framework. Procedia Comput. Sci. 19, 854–859 (2013)CrossRefGoogle Scholar
  13. 13.
    Mallig, N., Vortisch, P.: Measuring stability of mode choice behavior. In: TRB 96th Annual Meeting Compendium of Papers. Transportation Research Board of the National Academies, Washington, DC, No. 17–01942, Accepted for Publication in Transportation Research Record, January 2017Google Scholar
  14. 14.
    Mueller, K., Axhausen, K.: Hierarchical IPF: generating a synthetic population for Switzerland. In: ERSA Conference Papers. European Regional Science Association (2011)Google Scholar
  15. 15.
    Outwater, M., Charlton, B.: The San Francisco model in practice: validation, testing, and application. In: Paper and Presentation for the Transportation Research Board (TRB) Innovations in Travel Demand Modeling Conference (2006)Google Scholar
  16. 16.
    Timmermans, H., Arentze, T., Joh, C.H.: Analysing space-time behaviour: new approaches to old problems. Prog. Hum. Geogr. 26(2), 175–190 (2002)CrossRefGoogle Scholar
  17. 17.
    Train, K.E.: Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge (2009)CrossRefzbMATHGoogle Scholar
  18. 18.
    Vij, A., Carrel, A., Walker, J.L.: Incorporating the influence of latent modal preferences on travel mode choice behavior. Transp. Res. Part A: Policy Pract. 54, 164–178 (2013)Google Scholar
  19. 19.
    Yáñez, M.F., Cherchi, E., Ortúzar, J.D.D., Heydecker, B.G.: Inertia and shock effects on mode choice panel data: implications of the transantiago implementation. In: The 12th International Conference on Travel Behaviour Research, Jaipur, India, 13–18 December 2009Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute for Transport StudiesKarlsruhe Institute of TechnologyKarlsruheGermany

Personalised recommendations