Organic Control of Traffic Lights

  • Holger Prothmann
  • Fabian Rochner
  • Sven Tomforde
  • Jürgen Branke
  • Christian Müller-Schloer
  • Hartmut Schmeck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5060)


In recent years, Autonomic and Organic Computing have become areas of active research in the computer science community. Both initiatives aim at handling the growing complexity in technical systems by creating systems with adaptation and self-optimisation capabilities. One application scenario for such “life-like” systems is the control of road traffic signals in urban areas. This paper presents an organic approach to traffic light control and analyses its performance by an experimental validation of the proposed architecture which demonstrates its benefits compared to classical traffic control.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kephart, J.O., Chess, D.M.: The vision of Autonomic Computing. IEEE Computer 36(1), 41–50 (2003)Google Scholar
  2. 2.
    Schmeck, H.: Organic Computing – A new vision for distributed embedded systems. In: Proceedings of the 8th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC 2005), pp. 201–203 (2005)Google Scholar
  3. 3.
    Branke, J., Mnif, M., Müller-Schloer, C., Prothmann, H., Richter, U., Rochner, F., Schmeck, H.: Organic Computing – Addressing complexity by controlled self-organization. In: Margaria, T., Philippou, A., Steffen, B. (eds.) Proceedings of the 2nd International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2006), pp. 200–206 (2006)Google Scholar
  4. 4.
    Transportation Research Board Washington, D.C.: Highway Capacity Manual (2000)Google Scholar
  5. 5.
    Robertson, D.I., Bretherton, R.D.: Optimizing networks of traffic signals in real time – the SCOOT method. IEEE Transactions on Vehicular Technology 40(1), 11–15 (1991)CrossRefGoogle Scholar
  6. 6.
    Bretherton, R.D., Rai, G.I.: The use of SCOOT in low flow conditions. Traffic Engineering & Control 23(12), 574–576 (1982)Google Scholar
  7. 7.
    Sims, A.G., Dobinson, K.W.: The Sydney Coordinated Adaptive Traffic (SCAT) System – Philosophy and Benefits. Proceedings of the International Symposium on Traffic Control Systems 29(2), 19–41 (1980)Google Scholar
  8. 8.
    Friedrich, B.: Ein verkehrsadaptives Verfahren zur Steuerung von Lichtsignalanlagen. Veröffentlichung des Fachgebiets Verkehrstechnik und Verkehrsplanung. Technische Universität München (1999)Google Scholar
  9. 9.
    Wilson, S.W.: ZCS: A zeroth level classifier system. Evolutionary Computation 2(1), 1–18 (1994)CrossRefGoogle Scholar
  10. 10.
    Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)CrossRefGoogle Scholar
  11. 11.
    Wilson, S.W.: Get real! XCS with continuous-valued inputs. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 209–219. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Dam, H.H., Abbass, H.A., Lokan, C.: Be real! XCS with continuous-valued inputs. In: Rothlauf, F., et al. (eds.) Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation (GECCO 2005), pp. 85–87 (2005)Google Scholar
  13. 13.
    Stone, C., Bull, L.: For real! XCS with continuous-valued inputs. Evolutionary Computation 11(3), 299–336 (2003)CrossRefGoogle Scholar
  14. 14.
    Foy, M.D., Benekohal, R.F., Goldberg, D.E.: Signal timing determination using genetic algorithms. In: Transportation Research Record No. 1365, Transportation Research Board, pp. 108–115 (1992)Google Scholar
  15. 15.
    Stevanovic, A., Martin, P.T., Stevanovic, J.: VISGAOST: VISSIM-based genetic algorithm optimization of signal timings. In: Proceedings of the 86th Transportation Research Board Meeting (2007)Google Scholar
  16. 16.
    Sun, D., Benekohal, R.F., Waller, S.T.: Multi-objective traffic signal timing optimization using non-dominated sorting genetic algorithm. In: Proceedings of the IEE Intelligent Vehicles Symposium, pp. 198–203 (2003)Google Scholar
  17. 17.
    Branke, J., Goldate, P., Prothmann, H.: Actuated traffic signal optimization using evolutionary algorithms. In: Proceedings of the 6th European Congress and Exhibition on Intelligent Transport Systems and Services (ITS 2007) (2007)Google Scholar
  18. 18.
    Cao, Y.J., Ireson, N., Bull, L., Miles, R.: Distributed learning control of traffic signals. In: Oates, M.J., Lanzi, P.L., Li, Y., Cagnoni, S., Corne, D.W., Fogarty, T.C., Poli, R., Smith, G.D. (eds.) EvoIASP 2000, EvoWorkshops 2000, EvoFlight 2000, EvoSCONDI 2000, EvoSTIM 2000, EvoTEL 2000, and EvoROB/EvoRobot 2000. LNCS, vol. 1803, pp. 117–126. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  19. 19.
    Bull, L., Sha’Aban, J., Tomlinson, A., Addison, J.D., Heydecker, B.: Towards distributed adaptive control for road traffic junction signals using learning classifier systems. In: Bull, L. (ed.) Applications of Learning Classifier Systems, pp. 276–299. Springer, Heidelberg (2004)Google Scholar
  20. 20.
    Swiss Verkehrs-Systeme AG: VS-Plus webpage,
  21. 21.
    National Electrical Manufacturers Association: NEMA Standards Publication TS 2-2003 v02.06 – Traffic Controller Assemblies with NTCIP Requirements (2003)Google Scholar
  22. 22.
    Rochner, F., Prothmann, H., Branke, J., Müller-Schloer, C., Schmeck, H.: An organic architecture for traffic light controllers. In: Hochberger, C., Liskowsky, R. (eds.) Informatik 2006 – Informatik für Menschen. LNI, vol. P-93, pp. 120–127. Köllen Verlag (2006)Google Scholar
  23. 23.
    TSS Transport Simulation Systems: Aimsun webpage,

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Holger Prothmann
    • 1
  • Fabian Rochner
    • 2
  • Sven Tomforde
    • 2
  • Jürgen Branke
    • 1
  • Christian Müller-Schloer
    • 2
  • Hartmut Schmeck
    • 1
  1. 1.Karlsruhe Institute of Technology (KIT)Univ. Karlsruhe (TH) – Institute AIFBKarlsruheGermany
  2. 2.Institute of Systems EngineeringLeibniz Univ. HannoverHannoverGermany

Personalised recommendations