Distributed Regret Matching Algorithm for a Dynamic Route Guidance

  • Tai-Yu Ma
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 296)


This paper proposes a distributed self-learning algorithm based on the regret matching process in games for a dynamic route guidance. We incorporate a user’s past routing experiences and en-route traffic information into their optimal route guidance learning. The numerical study illustrates that the proposed self-guidance method can effectively reduce the travel times and delays of guided users in congested situation.


game multi-agent distributed learning route guidance Nash Equilibrium 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bottom, J.A.: Consistent anticipatory route guidance. Ph.D. thesis, Massachusetts Institute of Technology (2000)Google Scholar
  2. 2.
    Zuurbier, F.S.: Intelligent Route Guidance. PhD thesis, Technische Universiteit Delft (2010)Google Scholar
  3. 3.
    Kaufman, D.E., Smith, R.L., Wunderlich, K.E.: An iterative routing/assignment method for anticipatory real-time route guidance. In: IEEE Vehicle Navigation and Information Systems Conference, vol. 2, pp. 693–700 (1991)Google Scholar
  4. 4.
    Sarachik, P.E., Ozguner, U.: On Decentralized Dynamic Routing for Congested Traffic Networks. IEEE Transactions on Automatic Control AC-27(6), 1233–1238 (1982)Google Scholar
  5. 5.
    Minciardi, R., Gaetani, F.: A decentralized optimal control scheme for route guidance in urban road networks. In: IEEE Intelligent Transportation Systems Conference, pp. 1195–1199 (2001)Google Scholar
  6. 6.
    Deflorio, F.P.: Evaluation of a reactive dynamic route guidance strategy. Transportation Research Part C 11, 375–388 (2003)CrossRefGoogle Scholar
  7. 7.
    Peeta, S., Yu, J.: Adaptability of a hybrid route choice model to incorporating driver behavior dynamics under information provision. IEEE Transactions on Systems, Man, and Cybernetics Part A 34(2), 243–256 (2004)CrossRefGoogle Scholar
  8. 8.
    Jha, M., Madanat, S., Peeta, S.: Perception updating and day-to-day travel choice dynamics in traffic networks with information provision. Transportation Research Part C 6, 189–212 (1998)CrossRefGoogle Scholar
  9. 9.
    Fudenberg, D., Levine, D.K.: The Theory of Learning in Games. MIT Press, Cambridge (1998)MATHGoogle Scholar
  10. 10.
    Young, P.H.: Strategic Learning and Its Limit. Oxford University Press, Oxford (2005)Google Scholar
  11. 11.
    Monderer, D., Shapley, L.S.: Fictitious Play Property for Games with Identical Interests. Journal of Economic Theory 68, 258–265 (1996)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Hart, S., Mas-Colell, A.: A simple adaptive procedure leading to correlated equilibrium. Econometrica 68, 1127–1150 (2000)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Cominetti, R., Melo, E., Sorin, S.: A payoff-based learning procedure and its application to traffic games. Games and Economic Behavior 70(1), 71–83 (2010)CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Garcia, A., Reaume, D., Smith, R.L.: Fictitious play for finding system optimal routings in dynamic traffic networks. Transportation Research Part B 34, 147–156 (2000)CrossRefGoogle Scholar
  15. 15.
    Miyagi, T., Peque Jr., G.C.: Informed-user algorithms that converge to Nash equilibrium in traffic games. Procedia - Social and Behavioral Sciences 54, 438–449 (2012)CrossRefGoogle Scholar
  16. 16.
    Friesz, T.L., Bernstein, D., Smith, T., Tobin, R., Wie, B.: A variational inequality formulation of the dynamic network user equilibrium problem. Operations Research 41, 80–91 (1993)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Tong, C.O., Wong, S.C.: A predictive dynamic traffic assignment model in congested capacity-constrained road networks. Transportation Research Part B 34(8), 625–644 (2000)CrossRefGoogle Scholar
  18. 18.
    Kerner, B.S., Rehborn, H., Aleksic, M., Haug, A.: Traffic Prediction Systems in Vehicles. In: 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, pp. 72–77 (2005)Google Scholar
  19. 19.
    Kuwahara, M., Akamatsu, T.: Decomposition of the reactive assignments with queues for many-to-many origin-destination pattern. Transportation Research Part B 31(1), 1–10 (1997)CrossRefGoogle Scholar
  20. 20.
    Gawron, C.: An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. International Journal of Modern Physics C 9(3), 393–408 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CEPS/INSTEADEsch-sur-AlzetteLuxembourg

Personalised recommendations