Advertisement

Wide-Area Traffic Simulation Based on Driving Behavior Model

  • Yuu Nakajima
  • Yoshiyuki Nakai
  • Hattori Hiromitsu
  • Toru Ishida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5925)

Abstract

Multiagent-based simulations are a key part of several research fields. Multiagent-based simulations yield multiagent societies that well reproduce human societies, and so are seen as an excellent tool for analyzing the real world. A multiagent-based simulation allows crowd behavior to emerge through interactions among agents where each agent is affected by the emerging crowd behavior. The interaction between microscopic and macroscopic behaviors has long been considered an important issue, termed the “micro-macro problem”, in the field of sociology, but research on the issue is still premature in the engineering domain. We are focusing on citywide traffic as a target problem and are attempting to realize mega-scale multiagent-based traffic simulations. While macro-level simulations are popular in the traffic domain, it has been recognized that micro-level analysis is also beneficial. However, there is no software platform that can realize analyses based on both micro and macro viewpoints due to implementation difficulties. In this paper, we propose a traffic simulation platform that can execute citywide traffic simulations that include driving behavior models. Our simulation platform enables the introduction of individual behavior models while still retaining scalability.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jacyno, M., Bullock, S., Luck, M., Payne, T.: Emergent service provisioning and demand estimation through self-organizing agent communities. In: Proceedings of the 8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009), pp. 481–488 (2009)Google Scholar
  2. 2.
    Tesfatsion, L.S.: Introduction to the special issue on agent-based computational economics. Journal of Economic Dynamics & Control 25(3-4), 281–293 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Vasirani, M., Ossowski, S.: A market-inspired approach to reservation-based urban road traffic management. In: Proceedings of the 8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009), pp. 617–624 (2009)Google Scholar
  4. 4.
    Balmer, M., Cetin, N., Nagel, K., Raney, B.: Towards truly agent-based traffic and mobility simulations. In: 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004, pp. 60–67 (2004)Google Scholar
  5. 5.
    Halle, S., Chaib-draa, B.: A collaborative driving system based on multiagent modelling and simulations. Journal of Transportation Research Part C 13, 320–345 (2005)CrossRefGoogle Scholar
  6. 6.
    Panait, L.: A pheromone-based utility model for collaborative foraging. In: Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), pp. 36–43 (2004)Google Scholar
  7. 7.
    Ishida, T., Nakajima, Y., Murakami, Y., Nakanishi, H.: Augmented experiment: Participatory design with multiagent simulation. In: International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 1341–1346 (2007)Google Scholar
  8. 8.
    Tanaka, Y., Nakajima, Y., Hattori, H., Ishida, T.: A driver modeling methodology using hypothetical reasoning for multiagent traffic simulation. In: Ghose, A., Governatori, G., Sadananda, R. (eds.) PRIMA 2007. LNCS (LNAI), vol. 5044, pp. 278–287. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Hattori, H., Nakajima, Y., Ishida, T.: Agent modeling with individual human behaviors. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009), pp. 1369–1370 (2009)Google Scholar
  10. 10.
    Moyaux, T., Chaib-draa, B., D’Amours, S.: Multi-agent simulation of collaborative strategies in a supply chain. In: 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004, pp. 52–59 (2004)Google Scholar
  11. 11.
    Yamashita, T., Izumi, K., Kurumatani, K., Nakashima, H.: Smooth traffic flow with a cooperative car navigation system. In: AAMAS 2005, pp. 478–485. ACM Press, New York (2005)CrossRefGoogle Scholar
  12. 12.
    Poole, D.: Theorist: A logical reasoning system for defaults and diagnosis. In: The Knowledge Frontier. Springer, Heidelberg (1987)Google Scholar
  13. 13.
    Murakami, Y., Sugimoto, Y., Ishida, T.: Modeling human behavior for virtual training systems. In: AAAI 2005, pp. 127–132 (2005)Google Scholar
  14. 14.
    Illenberger, J., Flotterod, G., Nagel, K.: Enhancing matsim with capabilities of within-day re-planning. In: Intelligent Transportation Systems Conference (ITSC 2007), pp. 94–99 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yuu Nakajima
    • 1
  • Yoshiyuki Nakai
    • 1
  • Hattori Hiromitsu
    • 1
  • Toru Ishida
    • 1
  1. 1.Department of Social InformaticsKyoto UniversityKyotoJapan

Personalised recommendations