Applied Intelligence

, Volume 46, Issue 1, pp 113–123 | Cite as

Nested hybrid evolutionary model for traffic signal optimization



A noble Nested Hybrid Evolutionary Model is presented to reduce the wait time of vehicles at traffic signals and improve the mobility within the road network. In effect, it contributes towards achieving green environment and reducing the fuel consumption. The proposed model is based on Bi-level Stackelberg Game in which the upper layer is “traffic signals” which is optimized using evolutionary computational techniques (ACO, GA and a Hybrid of ACO and GA) and the lower layer is “stochastic user equilibrium” for which road network is designed using Petri Net (PN) respectively. A comparative analysis has been carried out and it was found that nested hybrid model outperforms ACO and GA.


Computational techniques Problem solving GA Ant Colony Genetic Algorithm Petri Net Game playing Traffic signal Optimization techniques 


  1. 1.
    Abdulaal M, LeBlanc LJ (1979) Continuous equilibrium network design models. Transp Res 13B(1):19–32CrossRefMATHGoogle Scholar
  2. 2.
    Allsop RE, Charlesworth JA (1977) Traffic in a signal-controlled road network: an example of different signal timings including different routings. Traffic Eng Control 18(5):262–264Google Scholar
  3. 3.
    Bar-Gera H, Hellman F, Patriksson M (2013) Computational precision of traffic equilibria sensitivities in automatic network design and road pricing. Procedia - Social Behav Sci 80:41–60. 20th International Symposium on Transportation and Traffic Theory (ISTTTCrossRefGoogle Scholar
  4. 4.
    Baskan O, Haldenbilen S Ant colony optimization approach for optimizing traffic signal timings. ISBN 978-953-(2011)-307-157-2Google Scholar
  5. 5.
    Baskan O, Haldenbilen S, Ceylan H, Ceylan H (2009) A new solution algorithm for improving performance of ant colony optimization. Appl Math Comput 211(1):75–84MathSciNetMATHGoogle Scholar
  6. 6.
    Cantarella GE, Improta G, Sforza A (1991) Iterative procedure for equilibrium network traffic signal setting. Transp Res 25A(5):241–249CrossRefGoogle Scholar
  7. 7.
    Ceylan H, Bell MGH (2004) Traffic signal timing optimization based on genetic algorithm approach, including drivers’ routing. Transp Res 38B(4):329–342CrossRefGoogle Scholar
  8. 8.
    Chen L-W, Hu T-Y (2011) Dynamic equilibrium for combined signal settings and dynamic traffic assignment. Asian Transp Stud 1(4):396–411Google Scholar
  9. 9.
    Dezanil H, Gomes L, Damianil F, Marranghello N. Controlling traffic jams on urban roads modeled in coloured petri net using genetic algorithm. In: IECON 2012—38th annual conference on IEEE industrial electronics societyGoogle Scholar
  10. 10.
    Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artificial Life, MIT Press, CambridgeGoogle Scholar
  11. 11.
    Dorigo M, Stutzle T (2004) Ant colony optimization. A Bradford Book. MIT Press, CambridgeMATHGoogle Scholar
  12. 12.
    Heydecker BG, Khoo TK (1990) The equilibrium network design problem. In: Proceedings of AIRO’90 conference on models and methods for decision support. Sorrento, pp 587–602Google Scholar
  13. 13.
    IRC: 64–1990, guidelines for road capacity in rural area, Indian Road congressGoogle Scholar
  14. 14.
    Josefsson M, Patriksson M (2007) Sensitivity analysis of separable traffic equilibria, with application to bilevel optimization in network design. Transp Res Part B 41:4–31Google Scholar
  15. 15.
    LeBlanc LJ (1975) An algorithm for the discrete network , vol 9Google Scholar
  16. 16.
    Lee C, Machemehl RB (1998) Genetic algorithm, local and iterative searches for combining traffic assignment and signal control, Traffic and Transportation Studies. In: Proceedings of ICTTS 98, pp 489–497Google Scholar
  17. 17.
    Putha R, Quadrifoglio L, Zechman E (2012) Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Comput-Aided Civ Infrastruct Eng 27:14–28CrossRefGoogle Scholar
  18. 18.
    Sahana SK, Jain A (2011) An improved modular hybrid ant colony approach for solving traveling salesman problem. Int J Comput (JoC) 1(2):123–127. doi: 10.5176-2010-2283_1.249. ISSN: 2010-2283Google Scholar
  19. 19.
    Sahana SK, Kumar K (2014) Hybrid synchronous discrete distance time model for traffic signal optimization, international. In: Series smart innovation, systems and technologies, vol-31. Book Computational Intelligence in Data Mining, Springer India, pp 23–33. Print: ISBN- 978-81-322-2204-0, Online: ISBN- 978-81-322-2205-7. doi: 10.1007/978-81-322-2205-7_3
  20. 20.
    Sahana SK, Jain A, Mahanti PK (2014) Ant colony optimization for train scheduling: an analysis. Int J Intell Syst Appl 6(2):29–36. doi: 10.5815/ijisa.2014.02.04. ISSN-2074-904X(print),2074-9058(online)Google Scholar
  21. 21.
    Shefi Y (1985) Urban transportation network: equilibrium analysis with mathematical programming method. Traffic engineering control. Prentice-Hall, ISBN 0-13-93-972Google Scholar
  22. 22.
    Suwansirikul F (1987) Tobin equilibrium decomposed optimization: a heuristic for the continuous equilibrium network design problem. Transp Sci 21(4):254–263CrossRefMATHGoogle Scholar
  23. 23.
    Yaqub O, Li L (2013) Modeling and analysis of connected traffic intersection based on modified binary Petri net. Hindawi Publishing Corporation. Int J Veh Technol Article ID 192516Google Scholar
  24. 24.
    Zhou S-P, Yan X-P (2009) The fusion algorithm of genetic and ant colony and its application. In: Fifth international conference on natural computation, 978-0-7695-3736-8/09Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceBITRanchiIndia

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