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Study of Traffic Flow Controlled with Independent Agent-Based Traffic Signals

  • Enrique Onieva
  • Vicente Milanés
  • Joshué Pérez
  • Javier Alonso
  • Teresa de Pedro
  • Ricardo García
  • Jorge Godoy
  • Jorge Villagra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6928)

Abstract

Dealing with urban traffic is a highly complex task since it involves the coordination of many actors. Traditional approaches attempt to optimize traffic signal control for a particular vehicle density; the main disadvantage lies in the fact that traffic changes constantly. Managing traffic congestion seems to be a problem of adaptation rather than of optimization. In this work we present an agent-based traffic simulator which represents a traffic grid with two-way roads of three exclusive lanes per direction, with intersections regulated by signals. We study the repercussions on traffic flow of simple parametric behaviours when each light operates independently. A dominance analysis is applied to compare the strategies.

Keywords

Traffic Signal Intelligent Transportation System Vehicle Density Vehicle Detector Left Turn 
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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References

  1. 1.
    Gokulan, B.P., Srinivasan, D.: Distributed geometric fuzzy multiagent urban traffic signal control. IEEE Transactions on Intelligent Transportation Systems 11(3), 714–726 (2010)CrossRefGoogle Scholar
  2. 2.
    Al-Khalili, A.J.: Urban traffic control- a general approach. IEEE Transactions on Systems Man and Cybernetics. 15(2), 260–271 (1985)CrossRefGoogle Scholar
  3. 3.
    Robertson, D.I.: Traffic Models and Optimum Strategies of Control: A Review. Proceedings on Traffic Control Systems 1, 276–289 (1979)Google Scholar
  4. 4.
    Webster, F.: Traffic signal settings. In: HMSO (1958)Google Scholar
  5. 5.
    Wunderlich, R., Elhanany, I., Urbanik, T.: A stable longest queue first signal scheduling algorithm for an isolated intersection. In: IEEE International Conference on Vehicular Electronics and Safety, pp. 1–6 (2007)Google Scholar
  6. 6.
    Wunderlich, R., Liu, C., Elhanany, I., UrbanikII, T.: A novel signal-scheduling algorithm with quality-of-service provisioning for an isolated intersection. IEEE Transactions on Intelligent Transportation Systems 9(3), 536–547 (2008)CrossRefGoogle Scholar
  7. 7.
    Sims, A., Dobinson, K.: SCAT-The Sydney Co-ordinated Adaptive Traffic System–Philosophy and Benefits. In: International Symposium on Traffic Control Systems, vol. 2 (1979)Google Scholar
  8. 8.
    Henry, J., Farges, J., Tuffal, J.: The PRODYN real time traffic algorithm. In: Proceedings of the 4th Conference on Control in Transportation Systems, vol. 2(1), p. 305 (1984)Google Scholar
  9. 9.
    Choy, M.C., Srinivasan, D., Cheu, R.: Cooperative, hybrid agent architecture for real-time traffic signal control. IEEE Transactions on Systems, Man and Cybernetics, Part A 33(5), 597–607 (2003)CrossRefGoogle Scholar
  10. 10.
    Wilensky, U., et al.: NetLogo (1999), http://ccl.northwestern.edu/netlogo
  11. 11.
    Fonseca, C., Fleming, P., et al.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Enrique Onieva
    • 1
  • Vicente Milanés
    • 1
  • Joshué Pérez
    • 1
  • Javier Alonso
    • 1
  • Teresa de Pedro
    • 1
  • Ricardo García
    • 1
  • Jorge Godoy
    • 1
  • Jorge Villagra
    • 1
  1. 1.AUTOPIA programCenter for Automation and Robotics (CAR)MadridSpain

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