Effect of Anticipatory Stigmergy on Decentralized Traffic Congestion Control

  • Takayuki Ito
  • Ryo Kanamori
  • Jun Takahashi
  • Ivan Marsa Maestre
  • Enrique de la Hoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7455)

Abstract

In this paper, we propose an anticipatory stigmergy model for decentralized traffic congestion management. Managing traffic congestion is one of the main issues for smart cities, and many works have been trying to address it from the IT and Transportation research perspectives. In the literature, there are a lot of studies and practices for observing traffic flow and providing stochastic estimation about traffic congestion. Recently, dynamic coordination methods are becoming possible using the more short-term traffic information that can be provided by probe-vehicle information or smart phones. Some approaches have been trying to handle short-term traffic information in which a stigmergy-based approach is employed as an indirect communication method for cooperation among distributed agents and for managing traffic congestion. One drawback of these approaches is that handling near-future congestion remains problematic because stigmergies are basically past information. In this paper, we propose anticipatory stigmergy for sharing information on near-future traffic. In this model, all vehicles submit their near-future intention as anticipatory stigmergy to reschedule their plans. Our preliminary results demonstrate that anticipatory stigmergy works well and robust even when road construction dynamically change the road network.

Keywords

Transportation Nism 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    wikipedia: Route assignment (2011) (online; accessed April 6, 2012)Google Scholar
  2. 2.
    Sheffi, Y.: Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Prentice-Hall (1985)Google Scholar
  3. 3.
    OSM: Openstreetmap (2012)Google Scholar
  4. 4.
    Ito, T., Kanamori, R., Takahashi, J., Maestre, I.M., de la Hoz, E.: The comparison of stigmergy strategies for decentralized traffic congestion control: Preliminary results. In: The Proceedings of the 12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012 (2012)Google Scholar
  5. 5.
    IBM: Ibm and singapore’s land transport authority pilot innovative traffic prediction tool (2007)Google Scholar
  6. 6.
    Wikipedia: Congestion pricing (2012) (online; accessed March 31, 2012)Google Scholar
  7. 7.
    Kockelman, K.M., Kalmanje, S.: Credit-based congestion pricing: a policy proposal and the public’s response. Transportation Research Part A, 671–690 (2005)Google Scholar
  8. 8.
    Gulipallia, P.K., Kockelman, K.M.: Credit-based congestion pricing: A dallas-fort worth application. Transport Policy (2008)Google Scholar
  9. 9.
    Dimitriou, L., Tsekeris, T.: Evolutionary game-theoretic model for dynamic congestion pricing in multi-class traffic networks. Netnomics (2009)Google Scholar
  10. 10.
    Chen, B., Cheng, H.H.: A review of the applications of agent technology in traffic and transportation systems. IEEE Transactions on Intelligent Transportation Systems (2010)Google Scholar
  11. 11.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press (2004)Google Scholar
  12. 12.
    Dorigo, M., Stützle, T.: Ant Colony Optimization: Overview and Recent Advances. Springer (2010)Google Scholar
  13. 13.
    Claes, R., Holvoet, T., Weyns, D.: A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. IEEE Transactions on Intelligent Transportation Systems 12(2), 364–373 (2011)CrossRefGoogle Scholar
  14. 14.
    Narzt, W., Wilflingseder, U., Pomberger, G., Kolb, D., Hortner, H.: Self-organising congestion evasion strategies using ant-based pheromones. Intelligent Transport Systems, IET 4(1), 93–102 (2010)CrossRefGoogle Scholar
  15. 15.
    Dallmeyer, J., Schumann, R., Lattner, A.D., Timm, I.J.: Don’t go with the ant flow: Ant-inspired traffic routing in urban environments. In: The Proceedings of the 8th Workshop on Agents in Traffic and Transportation, ATT 2012 (2012)Google Scholar
  16. 16.
    Morikawa, T., Miwa, T.: Preliminary analysis on dynamic route choice behavior using probe-vehicle data. Journal of Advanced Transportation (2006)Google Scholar
  17. 17.
    Pillac, V., Gendreau, M., Gueret, G., Medaglia, A.L.: A review of dynamic vehicle routing problems. Technical Report CIRRELT-2011-62 (2011)Google Scholar
  18. 18.
    Thomas, B.W., White III, C.C.: Anticipatory route selection. Transportation Science (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Takayuki Ito
    • 1
  • Ryo Kanamori
    • 1
  • Jun Takahashi
    • 1
  • Ivan Marsa Maestre
    • 2
  • Enrique de la Hoz
    • 2
  1. 1.Nagoya Institute of TechnologyNagoyaJapan
  2. 2.Computer Engineering DepartmentUniversidad de AlcalaAlcala de HenaresSpain

Personalised recommendations