Advertisement

Pheromone Model: Application to Traffic Congestion Prediction

  • Yasushi Ando
  • Osamu Masutani
  • Hiroshi Sasaki
  • Hirotoshi Iwasaki
  • Yoshiaki Fukazawa
  • Shinichi Honiden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3910)

Abstract

Social insects perform complex tasks without top-down style control, by sensing and depositing chemical markers called “pheromone”. We have examined applications of this pheromone paradigm towards intelligent transportation systems (ITS). Many of the current traffic management approaches require central processing with the usual risk for overload, bottlenecks and delays. Our work points towards a more decentralized approach that may overcome those risks. In this paper, a car is regarded as a social insect that deposits (electronic) pheromone on the road network. The pheromone represents density of traffic. We propose a method to predict traffic congestion of the immediate future through a pheromone mechanism without resorting to the use of a traffic control center. We evaluate our method using a simulation based on real-world traffic data and the results indicate applicability to prediction of immediate future traffic congestion. Furthermore, we describe the relationship between pheromone parameters and accuracy of prediction.

Keywords

Commercial Vehicle Intelligent Transportation System Short Route Alarm Pheromone Trail Pheromone 
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.
  2. 2.
    VICS(Vehicle Information and Communication System) Center, http://www.vics.or.jp/
  3. 3.
    Chung, E.: Classification of Traffic Pattern. In: Proc. of the 11th World Congress on ITS (2003)Google Scholar
  4. 4.
  5. 5.
    Yasdi, R.: Prediction of Road Traffic using a Neural Network Approach. In: Neural Comput Applic (1999). Springer-Verlag London Limited, Heidelberg (1999)Google Scholar
  6. 6.
    Zhang, C., Sun, S., Yu, G.: Short-Term Traffic Flow Forecasting Using Expanded Bayesian Network for Incomplete Data. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 950–955. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Karlson, P., Luscher, M.: Pheromones’: a new term for a class of biologically active substances. Nature (1959)Google Scholar
  8. 8.
    Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  9. 9.
    Sauter, J.A., Mattews, R., Van Parunak, H.D., Brueckner, S.: Evolving Adaptive Pheromone Path Planning Mechanisms. In: Proc. First International Conference on Autonomous Agents and Multi-Agent System(AAMAS 2002) (2002)Google Scholar
  10. 10.
    Van Dyke Parunak, H., Brueckner, S., Sauter, J., Prsdamer, J.: Mechanisms and Military Applications for Synthetic Pheromones. In: Proceedings of Workshop on Autonomy Oriented Computation, Agents (2001)Google Scholar
  11. 11.
    Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant Algorithms for Discrete Optimization, Artificial Life. MIT Press, Cambridge (1999)Google Scholar
  12. 12.
    Brueckner, S.: Return from the Ant: Synthetic Ecosystems for Manufacturing Control. Thesis at Humboldt University Department of Computer Science (2000)Google Scholar
  13. 13.
    Kaneko, K.: Period-doubling of Kink-antikink Patterns, Quasi-periodicity in Antiferro-like Structures and Spatial Intermittency in Coupled Map Lattices —toward a prelude to a Field Theory of Chaos. Prog. Theor. Phys. 72 (1984)Google Scholar
  14. 14.
    Kazama, T.: Method of Estimating Travel Time by Exchanging Time-Series Traffic Tables between Intersections. In: Proc. 11th World Congress on Intelligent Transport System (October 2004)Google Scholar
  15. 15.
    Kato, S., Tsugawa, S.: Information Transimission over Inter-Vehicle Communications.Technical Report of IEICE, ITS2002-31, 102th edn., pp. 13–18 (2002)(November 2002)Google Scholar
  16. 16.
    Saito, M., Funai, M., Umedu, T., Higashino, T.: Inter-Vehicle Ad-Hoc Communication Protocol for Acquiring Local Traffic Information. In: Proc. 11th World Congress on Intelligent Transport System (October 2004)Google Scholar
  17. 17.
    Horiguchi, R., Yoshii, T., Akahane, H., Kuwahara, M., Katakura, M., Ozaki, H., Oguchi, T.: A Benchmark DataSet for Validity Evaluation of Road Network Simulation Models. In: Proceedings of 5th World Congress on Intelligent Transport Systems (1998)Google Scholar
  18. 18.
    Howard, M., Payton, D., Estkowski, R.: Amorphous Predictive Nets. In: International Conference on Complex Systems, ICCS 2002 (2002)Google Scholar
  19. 19.
    Ben Akiva, M., Bierlaire, M., Koutsopoulos, H., Mishalani, R.: DynaMIT: a simulation-based system for traffic prediction. Paper presented at the DACCORD Short Term Forecasting Workshop (February 1998)Google Scholar
  20. 20.
    Campari, E.G., Levi, G.: A Realistic Simulation for Highway. In: Traffic by the Use of Cellular Automata, ICCS (2002)Google Scholar
  21. 21.
    Jayakrishnan, R., Mattingly, S.P., McNally, M.G.: Performance Study of SCOOT Traffic Control System with Non-ideal Detectorization: Field Operational Test in the City of Anaheim. In: 80th Annual Meeting of the Transportation Research Board (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yasushi Ando
    • 1
  • Osamu Masutani
    • 2
  • Hiroshi Sasaki
    • 2
  • Hirotoshi Iwasaki
    • 2
  • Yoshiaki Fukazawa
    • 1
  • Shinichi Honiden
    • 3
  1. 1.Department of Science and EngineeringWaseda UniversityTokyoJapan
  2. 2.DENSO IT Laboratory, Inc.Research and Development GroupTokyoJapan
  3. 3.National Institute of InformaticsTokyoJapan

Personalised recommendations