Autonomic Road Transport Support Systems pp 67-85

Part of the Autonomic Systems book series (ASYS) | Cite as

A Multiagent Approach to Modeling Autonomic Road Transport Support Systems

  • Maksims Fiosins
  • Bernhard Friedrich
  • Jana Görmer
  • Dirk Mattfeld
  • Jörg P. Müller
  • Hugues Tchouankem
Chapter

Abstract

In this chapter, we investigate a multiagent based approach to modeling autonomic features in urban traffic management. We provide a conceptual model of a traffic system comprising traffic participants modeled as locally autonomous agents, which act to optimize their operational and tactical decisions (e.g., route choice), and traffic management center(s) (TMC) which influence the traffic system according to dynamically selected policies. In this chapter, we focus on two autonomic features which emerge from the local decisions and actions of traffic participants and their interaction with the TMC and other vehicles: (1) Autonomic routing, in which we study how vehicle agents can individually adapt routing decisions based on local learning capabilities and traffic information communicated truthfully by a traffic management center; and (2) Autonomic grouping, i.e., collective decision-making of vehicles, which exchange route information and dynamically form and operate groups to drive in a convoy, thus aiming at higher speed and increased throughput. Communication is based on a (simulated) vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) protocols. Initial experiments are reported using a real-world traffic scenario modeled in the Aimsun software, which is extended by the decision logic of TMC and vehicles. The experiments indicate that autonomic routing and grouping can improve the performance of a traffic management network, even though negative effects such as unstable behavior can be observed in some cases.

Keywords

Autonomic grouping Autonomic rountig Communication Distributed systems Intelligent transport systems Multi-agent systems Traffic modelling 

References

  1. 1.
    Ahlbrecht, T., Dix, J., Köster, M., Kraus, P., Müller, J.P.: A scalable runtime platform for multiagent-based simulation. In: Dalpiaz, F., van Riemsdijk, M.B., Dix, J. (eds.) Engineering Multiagent Systems II. Lecture Notes in Artificial Intelligence, vol. 8758, pp. 81–102. Springer, Cham (2014)Google Scholar
  2. 2.
    Alvarez, L., Horowitz, R.: Safe platooning in automated highway systems, part i: safety regions design. Veh. Syst. Dyn. 32, 23–55 (1999)CrossRefGoogle Scholar
  3. 3.
    Alvarez, L., Horowitz, R.: Safe platooning in automated highway systems, part ii: velocity tracking controller. Veh. Syst. Dyn. 32, 57–84 (1999)CrossRefGoogle Scholar
  4. 4.
    Baskar, L., De Schutter, B., Hellendoorn, J., Papp, Z.: Traffic control and intelligent vehicle highway systems: a survey. IET Intell. Transp. Syst. 5(1), 38–52 (2011)CrossRefGoogle Scholar
  5. 5.
    Bazzan, A., Klügl, F.: A review on agent-based technology for traffic and transportation. Knowl. Eng. Rev. 29, 375–403 (2014)CrossRefGoogle Scholar
  6. 6.
    Bergenthal, T., Frommer, A., Paulerberg, D.: Wege auf Graphen. Mathe Prisma: Fachbereich C/Mathematik der Bergischen Universität Wuppertal (2004)Google Scholar
  7. 7.
    Brandt, F., Conitzer, V., Endriss, U.: Computational social choice. In: Weiss, G. (ed.) Multiagent Systems, 2nd edn., pp. 213–283. MIT Press, Cambridge (2013)Google Scholar
  8. 8.
    Burghout, W., Koutsopoulos, H.N., Andreasson, I.: A discrete-event mesoscopic traffic simulation model for hybrid traffic simulation. In: Proceedings of Intelligent Transportation Systems Conference (ITSC 2006), pp. 1102–1107. IEEE, Toronto (2006)Google Scholar
  9. 9.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press, New York (2001)MATHGoogle Scholar
  10. 10.
    Correa, J.R., Stier-Moses, N.E.: Wardrop equilibria. In: Cochran, J.J., Cox, L.A., Keskinocak, P., Kharoufeh, J.P., Smith, J.C. (eds.) Encyclopedia of Operations Research and Management Science. Wiley, New York (2011)Google Scholar
  11. 11.
    Desai, P., Loke, S., Desai, A., Singh, J.: Caravan: congestion avoidance and route allocation using virtual agent negotiation. Intell. Transp. Syst. 14(3), 1197–1207 (2013)CrossRefGoogle Scholar
  12. 12.
    Fiosina, J., Fiosins, M., Müller, J.P.: Decentralised Cooperative Agent-Based Clustering in Intelligent Traffic Clouds. Lecture Notes in Computer Science, vol. 8076, pp. 59–72. Springer, Berlin (2013)Google Scholar
  13. 13.
    Fiosins, M., Fiosina, J., Müller, J., Görmer, J.: Agent-based integrated decision making for autonomous vehicles in urban traffic. Adv. Intell. Soft Comput. 88, 173–178 (2011)CrossRefGoogle Scholar
  14. 14.
    Fiosins, M., Fiosina, J., Müller, J.P., Görmer, J.: Reconciling strategic and tactical decision making in agent-oriented simulation of vehicles in urban traffic. In: 4th Int. ICST Conf. on Simulation Tools and Techniques (SimuTools’2011) (2011)Google Scholar
  15. 15.
    Fiosins, M., Fiosina, J., Müller, J.P.: Change point analysis for intelligent agents in city traffic. In: Agents and Data Mining Interaction. Lecture Notes in Computer Science, vol. 7103. Springer, Berlin/Heidelberg (2012)Google Scholar
  16. 16.
    Fischer, K., Kuhn, N., Müller, J.P.: Distributed, knowledge-based, reactive scheduling in the transportation domain. In: Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Applications, pp. 47–53. IEEE, San Antonio (1994)Google Scholar
  17. 17.
    Görmer, J., Müller, J.P.: Multiagent system architecture and method for group-oriented traffic coordination. In: 6th IEEE International Conference on Digital Ecosystem Technologies - Complex Environment Engineering (DEST-CEE), pp. 1–6. IEEE, Campione d’Italia (2012)Google Scholar
  18. 18.
    Görmer, J., Ehmke, J.F., Fiosins, M., Schmidt, D., Schumacher, H., Tchouankem, H.: Decision support for dynamic city traffic management using vehicular communication. In: 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH), pp. 327–332. SciTePress, Noordwijkerhout (2011)Google Scholar
  19. 19.
    Kang, J., Kim, W., Lee, J., Yi, K.: Design, implementation, and test of skid steering-based autonomous driving controller for a robotic vehicle with articulated suspension. J. Mech. Sci. Technol. 24(3), 793–800 (2010)CrossRefGoogle Scholar
  20. 20.
    Kephart, J., Chess, D.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Müller, J.P.: The Design of Intelligent Agents. Lecture Notes in Artificial Intelligence, vol. 1177. Springer, Heidelberg (1996)Google Scholar
  22. 22.
    Padgham, L., Nagel, K., Singh, D., Chen, Q.: Integrating BDI agents into a MATSim simulation. In: European Conference on Artificial Intelligence (ECAI 2014), pp. 681–686. IOS Press, Prague (2014)Google Scholar
  23. 23.
    Papacostas, C.S., Prevedouros, P.D.: Transportation Engineering and Planning. Pearson Education, Upper Saddle River (2001)Google Scholar
  24. 24.
    Sanderson, D., Busquets, D., Pitt, J.: A micro-meso-macro approach to intelligent transportation systems. In: IEEE Sixth International Conference on Self-adaptive and Self-organizing Systems Workshops (SASOW), pp. 71–76. IEEE, Lyon (2012)Google Scholar
  25. 25.
    Sommer, C., Eckhoff, D., German, R., Dressler, F.: A computationally inexpensive empirical model of ieee 802.11p radio shadowing in urban environments. Technical Report CS-2010-06. Universitat Erlangen-Nurnberg, Erlangen (2010)Google Scholar
  26. 26.
    Tchouankem, H., Schmidt, D., Schumacher, H.: Impact of vehicular communication performance on travel time estimation in urban areas. In: 6th International Symposium “Networks for Mobility 2012”, Stuttgart (2012)Google Scholar
  27. 27.
    Tesauro, G., Chess, D.M., Walsh, W.E., Das, R., Segal, A., Whalley, I., Kephart, J.O., White, S.R.: A multi-agent systems approach to autonomic computing. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1, AAMAS ’04, pp. 464–471. IEEE Computer Society, Washington (2004)Google Scholar
  28. 28.
    VanMiddlesworth, M., Dresner, K., Stone, P.: Replacing the stop sign: unmanaged intersection control for autonomous vehicles. In: AAMAS Workshop on Agents in Traffic and Transportation, pp. 94–101, Estoril. http://www.cs.utexas.edu/users/ai-lab/?ATT08-vanmiddlesworth (2008)
  29. 29.
    Varaiya, P.: Smart cars on smart roads: problems of control. IEEE Trans. Autom. Control 38, 195–207 (1993)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Varga, L.Z.: Game theory model for autonomously self-adapting navigation. In: Kotsialos, A., Müller, J.P., Schumann, R., McCluskey, L., Rana, O., Klül, F. (eds.) Autonomic Road Transport Support Systems. Springer, Heidelberg (2015)Google Scholar
  31. 31.
    Vasirani, M.: A computational market for distributed control of urban road traffic systems. IEEE Trans. Intell. Transp. Syst. 12, 313–321 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Maksims Fiosins
    • 1
  • Bernhard Friedrich
    • 2
  • Jana Görmer
    • 1
  • Dirk Mattfeld
    • 3
  • Jörg P. Müller
    • 1
  • Hugues Tchouankem
    • 4
  1. 1.Department of InformaticsTU ClausthalClausthal-ZellerfeldGermany
  2. 2.Institute of Transportation and Urban EngineeringTechnische Universität BraunschweigBraunschweigGermany
  3. 3.Decision Support GroupTechnische Universität BraunschweigBraunschweigGermany
  4. 4.Institute of Communications TechnologyLeibniz Universität HannoverHannoverGermany

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