Agent-Based Simulation Versus Econometrics – from Macro- to Microscopic Approaches in Route Choice Simulation

  • Gustavo Kuhn Andriotti
  • Franziska Klügl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4196)


Econometrics is nowadays an established approach to the discrete choice problem relying on statistical methods. It is used in several fields, e.g. route choice modelling, telecommunication analysis, etc. Despite its advantages, there are also some drawbacks. Thus, alternatives for modelling human choice are sought, which can reproduce overall system behavior and be valid at microscopic level.

In this paper, we propose an agent-based approach inspired in econometric techniques producing similar results on the macro level from microscopic behavior. This work aims to be a step forward on searching an alternative for econometrics.


Econometric Model Discrete Choice Road Segment Route Choice Econometric Approach 
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.


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  1. 1.
    Fehler, M., Klügl, F., Puppe, F.: Approaches for resolving the dilemma between model structure refinement and parameter calibration in agent-based simulations. In: Proceedings of the second international joint conference on Autonomous Agents and Multi-Agent Systems, AAMAS, Hakodate, Japan (to appear, 2006)Google Scholar
  2. 2.
    Luce, R.D.: Individual Choice Behavior: A Theoretical Analysis. Dover Publications, New York (1959)MATHGoogle Scholar
  3. 3.
    Marschak, J.: Binary choice constraints on random utility indications. In: Arrow, K. (ed.) Stanford Symposium on Mathematical Methods in the Social Sciences, pp. 312–329. Stanford University Press, Stanford (1960)Google Scholar
  4. 4.
    Luce, D., Suppes, P.: Preferences, utility and subjective probability. In: Luce, R., Bush, R., Galanter, E. (eds.) Handbook of Mathematical Psychology, pp. 249–410. John Wiley and Sons, New York (1965)Google Scholar
  5. 5.
    McFadden, D.: Conditional logit analysis of qualitative choice behavior. Frontiers of Econometrics (1974)Google Scholar
  6. 6.
    Ben-Akiva, M.: The structure of travel demand models. PhD thesis. MIT (1973)Google Scholar
  7. 7.
    McFadden, D., Train, K.: Mixed mnl models of discrete response. Journal of Applied Econometrics 15, 447–470 (2000)CrossRefGoogle Scholar
  8. 8.
    Train, K.E.: Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge (2003)MATHCrossRefGoogle Scholar
  9. 9.
    Vrtic, M.: Simultanes Routen- und Verkehrsmittelwahlmodell. PhD thesis, Technischen Universität Dresdner (2003)Google Scholar
  10. 10.
    Klügl, F., Puppe, F.: The multi-agent simulation environment sesam. In: Workshops Simulation in Knowledge-based Systems, Reihe Informatik, Universität Paderborn, p. 194 (1998)Google Scholar
  11. 11.
    Rossetti, R.J.F., Liu, R.: A dynamic network simulation model based on multi-agent systems. In: ATT 2004, 3rd Workshop on Agents in Traffic and Transportation, AAMAS 2004, New York, pp. 88–93 (2004)Google Scholar
  12. 12.
    Dia, H.: An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transportation Research Part C: Emerging Technologies 10(5–6), 331–349 (2002)CrossRefGoogle Scholar
  13. 13.
    Bazzan, A.L.C., Klügl, F.: Route decision behaviour in a commuting scenario: Simple heuristics adaptation and effect of traffic forecast. In: Proceedings of the Euroworkshop on Behavioural Responses to ITS, Eindhoven (2003)Google Scholar
  14. 14.
    Bazzan, A.L., Klügl, F.: Case studies on the braess paradox: Simulating route recommendation and learning in abstract and microscopic models. Transportation Research Part C: Emerging Technologies 13(4), 299–319 (2005)CrossRefGoogle Scholar
  15. 15.
    Dia, H.: An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research 131(2), 253–261 (2001)MATHCrossRefGoogle Scholar
  16. 16.
    Ridwan, M.: Fuzzy preference based traffic assignment problem. Transportation Research Part C: Emerging Technologies 12(3-4), 209–233 (2004)CrossRefGoogle Scholar
  17. 17.
    Castelli, L., Pesenti, R., Ukovich, W.: Scheduling multimodal transportation systems. European Journal of Operational Research 155(3), 603–615 (2004)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gustavo Kuhn Andriotti
    • 1
  • Franziska Klügl
    • 1
  1. 1.Department of Artificial IntelligenceUniversity of WuerzburgWuerzburgGermany

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