Modeling a Multi-agent Self-organizing Architecture in MATSim

  • Youssef InedjarenEmail author
  • Besma Zeddini
  • Mohamed Maachaoui
  • Jean-Pierre Barbot
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 148)


The development of new information and communication technologies is contributing to the emergence of a new generation of real-time services in various fields of application. In the area of intelligent transport systems, these new services also include connected and autonomous vehicles that enable vehicles to collect and disseminate information, safety alerts and make driving smarter. In this paper, we propose a self-organizing architecture of agents and we project it on a multi-agent transport simulator (MATSim). In order to improve the performance of the DriverAgent in the simulation, an alternative approach to score the DriverAgent plans is proposed.


Autonomous vehicles Agent Multi-agent system Self-organization MATSim Scoring 


  1. 1.
    Ferber, J., Weiss, G.: Multi-agent Systems: An Introduction to Distributed Artificial Intelligence, vol. 1. Addison-Wesley Reading (1999)Google Scholar
  2. 2.
    Horni, A., Nagel, K., Axhausen, K. (eds.): Multi-Agent Transport Simulation MATSim. Ubiquity Press, London (2016). ISBN 978-1-909188-75-4, 978-1-909188-76-1, 978-1-909188-77-8, 978-1-909188-78-5. 10.5334/bawGoogle Scholar
  3. 3.
    Wenjie, C., Lifeng, C., Zhanglong, C., Shiliang, T.: A realtime dynamic traffic control system based on wireless sensor network. In: International Conference Workshops on Parallel Processing, 2005. ICPP 2005 Workshops, pp. 258–264. IEEE (2005)Google Scholar
  4. 4.
    Wiering, M.A.: Multi-agent reinforcement learning for traffic light control. In: Machine Learning: Proceedings of the Seventeenth International Conference (ICML’2000), pp. 1151–1158 (2000)Google Scholar
  5. 5.
    Dresner, K., Stone, P.: Multiagent traffic management: A reservation-based intersection control mechanism. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 2, pp. 530–537. IEEE Computer Society (2004)Google Scholar
  6. 6.
    Milanés, V., Pérez, J., Onieva, E., González, C.: Controller for urban intersections based on wireless communications and fuzzy logic. IEEE Trans. Intell. Transp. Syst. 11(1), 243–248 (2010)CrossRefGoogle Scholar
  7. 7.
    Balac, M., Janzen, M., Axhausen, K.W.: Alternative approach to scoring in matsim and how it affects activity rescheduling. In: Proceedings of the 97th Annual Meeting of the Transportation Research Board (TRB 2018). The National Academies of Sciences, Engineering, and Medicine (2018)Google Scholar
  8. 8.
    Feil, M., Balmer, M., Axhausen, K.W.: Enhancement and empirical estimation of matsims utility function. In: 9th STRC Swiss Transport Research Conference: Proceedings. Swiss Transport Research Conference (2009)Google Scholar
  9. 9.
    Joh, C.-H.: Measuring and predicting adaptation in multidimensional activity-travel patterns. Ph.D thesis, Technische Universiteit Eindhoven (2004).
  10. 10.
    Brooks, R.A.: How to build complete creatures rather than isolated cognitive simulators. In: Architectures for intelligence, pp. 239–254. Psychology Press (2014)Google Scholar
  11. 11.
    Di Marzo Serugendo, G., Gleizes, M.-P., Karageorgos, A.: Self-organization in multi-agent systems. Knowl. Eng. Rev. 20(2), 165–189 (2005)CrossRefGoogle Scholar
  12. 12.
    Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K.: Matsim-t: Architecture and simulation times. In: Multi-agent Systems For Traffic And Transportation Engineering, pp. 57–78. IGI Global (2009)Google Scholar
  13. 13.
    Balmer, M.: Travel demand modeling for multi-agent transport simulations: Algorithms and systems. Ph.D thesis, ETH Zurich (2007).
  14. 14.
    Nagel, K., Flötteröd, G.: Agent-based traffic assignment: Going from trips to behavioural travelers. In: Travel Behaviour Research in an Evolving World–Selected papers from the 12th International Conference On Travel Behaviour Research, pp. 261–294. International Association for Travel Behaviour Research (2012)Google Scholar
  15. 15.
    Charypar, D., Nagel, K.: Generating complete all-day activity plans with genetic algorithms. Transportation 32(4), 369–397 (2005)CrossRefGoogle Scholar
  16. 16.
    The Transport Systems Planning and Transport Telematics group of Technische Universitt Berlin. The matsim open berlin scenario (2018).

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Youssef Inedjaren
    • 1
    Email author
  • Besma Zeddini
    • 1
  • Mohamed Maachaoui
    • 1
  • Jean-Pierre Barbot
    • 2
  1. 1.Quartz LaboratoryEISTI, COMUE Paris SeineCergyFrance
  2. 2.Quartz LaboratoryENSEA, COMUE Paris SeineCergyFrance

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