Signal Setting Design at a Single Junction Through the Application of Genetic Algorithms

  • Giulio Erberto Cantarella
  • Stefano de Luca
  • Roberta Di Pace
  • Silvio Memoli
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 262)


The purpose of this chapter is the application of Genetic Algorithms to solve the Signal Setting Design at a single junction. Two methods are compared: the monocriteria and the multicriteria optimisations. In the former case, three different objectives functions were considered: the capacity factor maximisation, the total delay minimisation and the total number of stops minimisation; in the latter case, two combinations of criteria were investigated: the total delay minimisation and the capacity factor maximisation, the total delay minimisation and the total number of stops minimisation. Furthermore, two multicriteria genetic algorithms were compared: the Goldberg’s Pareto Ranking (GPR) and the Non Dominated Sorting Genetic Algorithms (NSGA-II). Conclusions discuss the effectiveness of multicriteria optimisation with respect to monocriteria optimisation, and the effectiveness of NSGA-II with respect to the GPR.


Signal setting Optimisation modelling Genetic algorithms Metaheuristics Single junction 


  1. 1.
    Akcelik, R.: Traffic Signals: Capacity and Timing Analysis. Research Report ARR No. 123. ARRB Transport Research Ltd., Vermont South, Australia (1981)Google Scholar
  2. 2.
    Allsop, R.E.: Delay minimising settings for fixed time traffic signals at a single junction. J. Inst. Math. Appl., 8, 164–185 (1971)Google Scholar
  3. 3.
    Allsop, R.E.: Estimating the traffic capacity of a signalized road junction. Transp. Res., 6, 245–255 (1972)Google Scholar
  4. 4.
    Baker, J.E.: Adaptive selection methods for genetic algorithms. Proceedings of the 3rd International Conference on Genetic Algorithms and Applications. In: Grefenstette, J.J. (ed.), New Jersey. Lawrence Erlbaum: Hillsdale, pp. 100–111 (1985)Google Scholar
  5. 5.
    Benekohal, R.F., Waller, S.T.: Multiobjective traffic signal timing optimization using non-dominated sorting genetic algorithm. In: Intelligent Vehicle Symposium, Proceedings IEEE 9, pp. 198–203 (2003)Google Scholar
  6. 6.
    Cantarella, G.E., Improta, G.: A nonlinear model for control system design at an individual signalized junction. Proceedings of the Conference of the Operation Research Italian Society , pp. 709–722 (1983)Google Scholar
  7. 7.
    Cantarella, G.E., Improta, G.: Capacity factor or cycle time optimization for signalized junctions: a graph theory approach. Transp. Res. B, 22B, 1–23 (1988)Google Scholar
  8. 8.
    Cantarella, G.E., Di Pace, R., Memoli, S., de Luca, S.: The network signal setting problem: the coordination approach vs. the synchronisation approach. Computer Modelling and Simulation (UKSim), 2013 UKSim 15th International Conference, pp. 575–579 (2013a). doi: 10.1109/UKSim.2013.99
  9. 9.
    Cantarella, G.E., Di Pace, R., Memoli, S., de Luca, S.: The application of multicriteria genetic algorithms for signal setting design at a single junction. 8th EUROSIM Congress on Modelling and Simulation, pp. 472–477 (2013b). doi: 10.1109/Eurosim.2013.85
  10. 10.
    Cantarella, G.E., de Luca, S., Di Gangi, M., Di Pace, R.: Stochastic equilibrium assignment with variable demand: literature review, comparisons and research needs. WIT Trans. Built Environ. 130, 349–364 (2013)CrossRefGoogle Scholar
  11. 11.
    Ceylan, H., Bell, M.G.H.: Genetic algorithm solution for the stochastic equilibrium transportation networks under congestion. Transp. Res. Part B 39, 169–185 (2005)CrossRefGoogle Scholar
  12. 12.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)Google Scholar
  13. 13.
    Foy, M.D., Benekohal, R.F., Goldberg, D.E.: Signal timing determination using genetic algorithms. Transp. Res. Rec. 1365, 108–115 (1992)Google Scholar
  14. 14.
    Gallivan, S., Heydecker, B.G.: Optimising the control performance of traffic signals at a single junction. Transportation Research B, 8, 357-370 (1988)Google Scholar
  15. 15.
    Gazis, D.C.: Optimal control of a system of oversaturated intersections. Oper. Res. 12(6), 815–831 (1964)Google Scholar
  16. 16.
    Girianna, M., Benekohal, R.F.: Using genetic algorithms to design signal coordination for oversaturated networks. Intell. Transp. Syst. 8, 117–129 (2004)CrossRefMATHGoogle Scholar
  17. 17.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  18. 18.
    Holland, J.H.: Adaptation in Natural and Artificial System. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  19. 19.
    Improta, G., Cantarella, G.E.: Control system design for an individual signalized junction, Transp. Res. B, 18, 147–168 (1984)Google Scholar
  20. 20.
    Michalopoulos, P., Stephanopolos, G.: Oversaturated signal system with queue length constraints. Transp. Res. 11, 413–421 (1977)Google Scholar
  21. 21.
    Park, B., Messer, C.J., Urbanik II, T.: Traffic signal optimization program for oversaturated conditions: genetic algorithms approach. Transp. Res. Rec. 1683, 133–142 (1999)CrossRefGoogle Scholar
  22. 22.
    Putha, R., Quadrifoglio, L., Zechman, E.: Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Comput Aided Civil Infrastruct. Eng. 27, 14–28 (2012)Google Scholar
  23. 23.
    Renfrew, D., Xiao-Hua, Yu.: Traffic Signal Optimization Using Ant Colony Algorithm, pp. 1–7. IEEE, Brisbane (2012)Google Scholar
  24. 24.
    Sun, D., Benekohal, R.F., Waller, S.T.: Multiobjective traffic signal timing optimization using non-dominated sorting genetic algorithm in Intelligent Vehicle Symposium. Proc. IEEE 9, 198–203 (2003)Google Scholar
  25. 25.
    Sun, D., Benekohal, R.F., Waller, S.T.: Bi-level programming formulation and heuristic solution approach for dynamic traffic signal optimization. Comput. Aided Civil Infrastr. Eng. 21, 321–333 (2003)CrossRefGoogle Scholar
  26. 26.
    Teklu, F., Sumalee, A., Watling, D.P.: A genetic algorithm approach for optimising traffic control signals considering routing. J. Comput. Aided Civil Infrastr. Eng. 22, 31–43 (2007)CrossRefGoogle Scholar
  27. 27.
    Webster, F.V.: Traffic signal settings. Road Research Technical Paper, 39, HMSO, LondonGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Giulio Erberto Cantarella
    • 1
  • Stefano de Luca
    • 1
  • Roberta Di Pace
    • 1
  • Silvio Memoli
    • 1
  1. 1.Department of Civil EngineeringUniversity of SalernoFiscianoItaly

Personalised recommendations