Advertisement

Re-routing Agents in an Abstract Traffic Scenario

  • Ana L. C. Bazzan
  • Franziska Klügl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5249)

Abstract

Human drivers may perform replanning when facing traffic jams or when informed that there are expected delays on their planned routes. In this paper, we address the effects of drivers re-routing, an issue that has been ignored so far. We tackle re-routing scenarios, also considering traffic lights that are adaptive, in order to test whether such a form of co-adaptation may result in interferences or positive cumulative effects. An abstract route choice scenario is used which resembles many features of real world networks. Results of our experiments show that re-routing indeed pays off from a global perspective as the overall load of the network is balanced. Besides, re-routing is useful to compensate an eventual lack of adaptivity regarding traffic management.

Keywords

Travel Time Short Path Route Choice Short Path Algorithm Human Driver 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balmer, M., Cetin, N., Nagel, K., Raney, B.: Towards truly agent-based traffic and mobility simulations. In: Jennings, N., Sierra, C., Sonenberg, L., Tambe, M. (eds.) Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multi Agent Systems, AAMAS, New York, USA, July 2004, vol. 1, pp. 60–67. IEEE Computer Society, New York (2004)Google Scholar
  2. 2.
    Bazzan, A.L.C., de Oliveira, D., Klügl, F., Nagel, K.: Adapt or not to adapt – consequences of adapting driver and traffic light agents. In: Tuyls, K., Nowe, A., Guessoum, Z., Kudenko, D. (eds.) ALAMAS 2005, ALAMAS 2006, and ALAMAS 2007. LNCS (LNAI), vol. 4865, pp. 1–14. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Bazzan, A.L.C., Klügl, F.: Case studies on the Braess paradox: simulating route recommendation and learning in abstract and microscopic models. Transportation Research C 13(4), 299–319 (2005)CrossRefGoogle Scholar
  4. 4.
    Bazzan, A.L.C., Klügl, F., Nagel, K.: Adaptation in games with many co-evolving agents. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 195–206. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Dia, H., Panwai, S.: Modelling drivers’ compliance and route choice behaviour in response to travel information. Special issue on Modelling and Control of Intelligent Transportation Systems, Journal of Nonlinear Dynamics 49(4), 493–509 (2007)zbMATHGoogle Scholar
  6. 6.
    Illenberger, J., Flötteröd, G., Nagel, K.: Enhancing matsim with capabilities of within-day re-planning. Technical Report 07–09, VSP Working Paper, TU Berlin, Verkehrssystemplanung und Verkehrstelematik (2007)Google Scholar
  7. 7.
    Klügl, F., Bazzan, A.L.C.: Route decision behaviour in a commuting scenario. Journal of Artificial Societies and Social Simulation 7(1) (2004)Google Scholar
  8. 8.
    Ortúzar, J., Willumsen, L.G.: Modelling Transport, 3rd edn. John Wiley & Sons, Chichester (2001)Google Scholar
  9. 9.
    Peeta, S., Yu, J.W.: A hybrid model for driver route choice incorporating en-route attributes and real-time information effects. Networks and Spatial Economics 5, 21–40 (2005)CrossRefzbMATHGoogle Scholar
  10. 10.
    Rossetti, R., Liu, R.: A dynamic network simulation model based on multi-agent systems. In: Applications of Agent Technology in Traffic and Transportation, pp. 88–93. Birkhäser (2005)Google Scholar
  11. 11.
    Tumer, K., Wolpert, D.: A survey of collectives. In: Tumer, K., Wolpert, D. (eds.) Collectives and the Design of Complex Systems, pp. 1–42. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Wahle, J., Bazzan, A.L.C., Klügl, F., Schreckenberg, M.: Decision dynamics in a traffic scenario. Physica A 287(3–4), 669–681 (2000)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ana L. C. Bazzan
    • 1
  • Franziska Klügl
    • 2
  1. 1.Instituto do Informatica, UFRGSPorto AlegreBrazil
  2. 2.Dep. of Artificial IntelligenceUniversity of WürzburgWürzburgGermany

Personalised recommendations