Multi-model Based Simulation Platform for Urban Traffic Simulation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7057)


Multiagent-based simulations are regarded as a useful technology for analyzing complex social systems; for example, traffic in a city. Traffic in a city has various aspects such as route planning on the road network and driving operations on a certain road. Both types of human behavior are being studied separately by specialists in their respective domains. We believe that traffic simulation platforms should integrate the various paradigms underlying agent decision making and the target environment. We focus on urban traffic as the target problem and attempt to realize a multiagent simulation platform based on the multi-model approach. While traffic flow simulations using simple agents are popular in the traffic domain, it has been recognized that driving behavior simulations with sophisticated agents are also beneficial. However, there is no software platform that can integrate traffic simulators dealing with different aspects of urban traffic. In this paper, we propose a traffic simulation platform that can execute citywide traffic simulations that take account of the aspects of route selection on a road network and driving behavior on individual roads. The proposed simulation platform enables the multiple aspects of city traffic to be reproduced while still retaining scalability.


Road Network Multiagent System Driving Behavior Simulation Platform Route Selection 
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.


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  1. 1.
    Balmer, M., Cetin, N., Nagel, K., Raney, B.: Towards truly agent-based traffic and mobility simulations. In: The 3rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2004), pp. 60–67 (2004)Google Scholar
  2. 2.
    Balmer, M., Meister, K., Rieser, M., Nagel, K., Axhausen, K.W.: Agent-based simulation of travel demand: Structure and computational performance of matsim-t. In: The 2nd TRB Conference on Innovations in Travel Modeling (2008)Google Scholar
  3. 3.
    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
  4. 4.
    Deguchi, H., Kanatani, Y., Kaneda, T., Koyama, Y., Ichikawa, M., Tanuma, H.: Social simulation design for pandemic protection. In: The First World Congress on Social Simulation (WCSS-2006), vol. 1, pp. 21–28 (2006)Google Scholar
  5. 5.
    Epstein, J., Axtell, R.: Growing Artificial Societies: Social Science from the Bottom Up. MIT Press (1996)Google Scholar
  6. 6.
    Hattori, H., Nakajima, Y., Ishida, T.: Learning from humans: Agent modeling with individual human behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part A 41(1), 1–9 (2011)CrossRefGoogle Scholar
  7. 7.
    Illenberger, J., Flotterod, G., Nagel, K.: Enhancing matsim with capabilities of within-day re-planning. In: The IEEE Intelligent Transportation Systems Conference, pp. 94–99 (2007)Google Scholar
  8. 8.
    Kitano, H., Tadokor, S., Noda, H., Matsubara, I., Takahasi, T., Shinjou, A., Shimada, S.: Robocup rescue: search and rescue in large-scale disasters as a domain for autonomous agents research. In: The IEEE Conference on Systems, Men, and Cybernetics, Tokyo, vol. VI, pp. 739–743 (October 1999),
  9. 9.
    Paruchuri, P., Pullalarevu, A.R., Karlapalem, K.: Multi agent simulation of unorganized traffic. In: The 1st International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-2002), pp. 176–183 (2002)Google Scholar
  10. 10.
    Raney, B., Nagel, K.: Iterative route planning for large-scale modular transportation simulations. Future Generation Computer Systems 20(7), 1101–1118 (2004)CrossRefGoogle Scholar
  11. 11.
    Scerri, D., Hickmott, S., Padgham, L., Drogoul, A.: An Architecture for Modular Distributed Simulation with Agent-Based Models. In: Ninth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-2010), pp. 541–548 (2010)Google Scholar
  12. 12.
    Yamamoto, G., Tai, H., Mizuta, H.: A Platform for Massive Agent-Based Simulation and Its Evaluation. In: Jamali, N., Scerri, P., Sugawara, T. (eds.) MMAS 2006, LSMAS 2006, and CCMMS 2007. LNCS (LNAI), vol. 5043, pp. 1–12. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Social InformaticsKyoto UniversitySakyo-kuJapan

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