Comparative Analysis of Selected Algorithms in the Process of Optimization of Traffic Lights

  • K. Małecki
  • P. Pietruszka
  • S. Iwan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10192)


Optimal settings of traffic lights and traffic light cycles are important tasks of modeling a modern ordered traffic in smart cities. This article analyzes the comparative effectiveness of selected optimization algorithms for the identified area. In particular, it involves the comparison of the concepts of genetic algorithm using particle swarm optimization, the differential evolution and the Monte Carlo method with two new approaches: evolution strategy involving the adaptation of the covariance matrix and topology archipelago consisting of four islands - different algorithms to optimize the length of the phase in fixed time traffic signals. Developed simulation solutions allowed to achieve a quantitative improvement in the selection of the optimal durations of the phases of traffic lights for the tested roads with junctions.


Intelligent transportation systems Simulation platform Traffic efficiency Traffic simulators 


  1. 1.
    Cao, C., Cui, F., Guo, G.: Two-direction green wave control of traffic signal based on particle swarm optimization. Appl. Mech. Mater. 26–28, 507–511 (2010)CrossRefGoogle Scholar
  2. 2.
    Michalopoulos, P.G., Stephanopoulos, G.: Oversaturated signal systems with queue length constraints—II. Transp. Res. 11(6), 423–428 (1977)CrossRefGoogle Scholar
  3. 3.
    Tobita, K., Nagatani, T.: Green-wave control of an unbalanced two-route traffic system with signals. Physica A: Stat. Mech. Appl. 392(21), 5422–5430 (2013)CrossRefGoogle Scholar
  4. 4.
    Ye, B.-L., Wu, W., Zhou, X., Mao, W., Huang, Y.-S.: A green wave band based method for urban arterial signal control. In: Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC (2014)Google Scholar
  5. 5.
    Zhang, Y., Huang, G.S.: Based on road green wave effect of collaborative strategy of signal timing fuzzy control. Appl. Mech. Mater. 321–324, 1836–1841 (2013)Google Scholar
  6. 6.
    Nagatani, T.: Vehicular traffic through a sequence of green-wave lights. Physica A: Stat. Mech. Appl. 380(1–2), 503–511 (2007)CrossRefGoogle Scholar
  7. 7.
    Kong, X., Shen, G., Xia, F., Lin, C.: Urban arterial traffic two-direction green wave intelligent coordination control technique and its application. Int. J. Control Autom. Syst. 9(1), 60–68 (2011)CrossRefGoogle Scholar
  8. 8.
    Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies: Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)zbMATHGoogle Scholar
  9. 9.
    Kwatirayo, S., Almhana, J., Liu, Z., Siblini, J.: Optimizing road intersection traffic flow using stochastic and heuristic algorithms. In: IEEE International Conference on Communications, ICC 2014 6883382, pp. 586–591 (2014)Google Scholar
  10. 10.
    De Oliveira, M.B.W., De Almeida Neto, A.: Optimization of traffic lights timing based on artificial neural networks. In: 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 6957986, pp. 1921–1922 (2014)Google Scholar
  11. 11.
    Kanungo, A., Sharma, A., Singla, C.: Smart traffic lights switching and traffic density calculation using video processing. In: Recent Advances in Engineering and Computational Sciences, RAECS 6799542 (2014)Google Scholar
  12. 12.
    Goel, S., Bush, S.F., Ravindranathan, K.: Self-organization of traffic lights for minimizing vehicle delay. In: 2014 – Proceedings International Conference on Connected Vehicles and Expo, ICCVE 7297692, pp. 931–936 (2015)Google Scholar
  13. 13.
    Odeh, S.M.: Hybrid algorithm: fuzzy logic-genetic algorithm on traffic light intelligent system. In: IEEE International Conference on Fuzzy Systems, 7338117 (2015)Google Scholar
  14. 14.
    Zhang, Y., Su, R., Gao, K.: Urban road traffic light real-time scheduling. In: Proceedings of the IEEE Conference on Decision and Control, 7402642, pp. 2810–2815 (2016)Google Scholar
  15. 15.
    Qi, L., Zhou, M., Luan, W.: An emergency traffic light strategy to prevent traffic congestion. In: ICNSC 2016 – 13th IEEE International Conference on Networking, Sensing and Control 7479013 (2016)Google Scholar
  16. 16.
    Kalganova, T., Russell, G., Cumming, A.: Multiple traffic signal control using a genetic algorithm. In: Dobnikar, A., Steele, N.C., Pearson, D.W., Albrecht, R.F. (eds.) Artificial Neural Nets and Genetic Algorithms, pp. 220–228. Springer, Vienna (1999)CrossRefGoogle Scholar
  17. 17.
    García-Nieto, J., Alba, E., Olivera, A.C.: Swarm intelligence for traffic light scheduling application to real urban areas. Eng. Appl. Artif. Intell. 25(2), 274–283 (2012)CrossRefGoogle Scholar
  18. 18.
    Gaca, S., Suchorzewski, W., Tracz, M.: Traffic engineering. Theory and practice, Wydawnictwa Komunikacji i Łączności (2008) [in Polish]Google Scholar
  19. 19.
    Robertson, D., Bretherton, R.: Optimizing networks of traffic signals in real time - the SCOOT method. IEEE Trans. Veh. Technol. 40(1), 11–15 (1991)CrossRefGoogle Scholar
  20. 20.
    Krajzewicz, D., Hertkorn, G., Rössel, C., Wagner, P.: SUMO (Simulation of Urban MObility) - an open-source traffic simulation. In: Proceedings of MESM 2002 (2002)Google Scholar
  21. 21.
    Krajzewicz, D., Brockfeld, E., Mikat, J., Ringel, J.: Simulation of modern traffic lights control systems using the open source traffic simulation SUMO. In: Industrial Simulation (2005)Google Scholar
  22. 22.
    Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: SUMO – simulation of urban mobility, an overview. In: The Third International Conference on Advances in System Simulation – SIMUL (2011)Google Scholar
  23. 23.
    Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 5(3&4), 128–138 (2012)Google Scholar
  24. 24.
    Gu, W., Ito, T.: Optimization of road distribution for traffic system based on vehicle’s priority. In: Booth, R., Zhang, M.-L. (eds.) PRICAI 2016. LNCS (LNAI), vol. 9810, pp. 729–737. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-42911-3_61 CrossRefGoogle Scholar
  25. 25.
    Cárdenas-Benítez, N., Aquino-Santos, R., Magaña-Espinoza, P., Edwards-Block, A., Cass, A.M.: Traffic congestion detection system through connected vehicles and big data. Sensors (Switzerland) 16(5), 599 (2016). Open AccessCrossRefGoogle Scholar
  26. 26.
    SUMO Simulation of Urban MObility.
  27. 27.
    Storn, R., Price, K.: Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Beyer, H.-G., Schwefel, H.-P.: Evolution strategies: a comprehensive introduction. Nat. Comput. 1(1), 3–52 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Hansen, N.: The CMA evolution strategy: a tutorial. CoRR, abs/1604.00772 (2016)Google Scholar
  30. 30.
    Rucinski, M., Izzo, D., Biscani F.: On the impact of the migration topology on the island model. CoRR, abs/1004.4541 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer ScienceWest Pomeranian University of TechnologySzczecinPoland
  2. 2.Maritime University of SzczecinSzczecinPoland

Personalised recommendations