Smart Mobility by Optimizing the Traffic Lights: A New Tool for Traffic Control Centers

  • Yesnier BravoEmail author
  • Javier Ferrer
  • Gabriel Luque
  • Enrique Alba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9704)


Urban traffic planning is a fertile area of Smart Cities to improve efficiency, environmental care, and safety, since the traffic jams and congestion are one of the biggest sources of pollution and noise. Traffic lights play an important role in solving these problems since they control the flow of the vehicular network at the city. However, the increasing number of vehicles makes necessary to go from a local control at one single intersection to a holistic approach considering a large urban area, only possible using advanced computational resources and techniques. Here we propose HITUL, a system that supports the decisions of the traffic control managers in a large urban area. HITUL takes the real traffic conditions and compute optimal traffic lights plans using bio-inspired techniques and micro-simulations. We compare our system against plans provided by experts. Our solutions not only enable continuous traffic flows but reduce the pollution. A case study of Málaga city allows us to validate the approach and show its benefits for other cities as well.


Traffic lights planning Multi-objective optimization Smart mobility 



The authors have been partially funded by project number 8.06/5.47.4142 in collaboration with the VSB-Technical University of Ostrava, the University of Málaga (Andalucá Tech), and by the Spanish MINECO project TIN2014-57341-R (


  1. 1.
    Alba, E., Blum, C., Asasi, P., Leon, C., Gomez, J.A.: Optimization Techniques for Solving Complex Problems, vol. 76. Wiley, Hoboken (2009)CrossRefGoogle Scholar
  2. 2.
    Aldridge Traffic Controllers: SCATS. Technical report, Australia (2015)Google Scholar
  3. 3.
    Bäck, T., Fogel, D.B., Michalewicz, Z.: Handbook of Evolutionary Computation. Oxford University Press, New York (1997)CrossRefzbMATHGoogle Scholar
  4. 4.
    Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo-simulation of urban mobility. In: The Third International Conference on Advances in System Simulation (SIMUL 2011), Barcelona, Spain (2011)Google Scholar
  5. 5.
    Bodenheimer, R., Brauer, A., Eckhoff, D., German, R.: Enabling GLOSA for adaptive traffic lights. In: 2014 IEEE Vehicular Networking Conference (VNC), pp. 167–174. IEEE (2014)Google Scholar
  6. 6.
    Boussaïd, I., Lepagnot, J., Siarry, P.: A survey on optimization metaheuristics. Inf. Sci. 237, 82–117 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Zin, C.: eDaptiva: full-featured urban traffic management center. Technical report, Czerch republic (2015)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Garcia-Nieto, J., Olivera, A.C., Alba, E.: Optimal cycle program of traffic lights with particle swarm optimization. IEEE Trans. Evol. Comput. 17(6), 823–839 (2013)CrossRefGoogle Scholar
  10. 10.
    Glover, F.W., Kochenberger, G.A.: Handbook of Metaheuristics, vol. 57. Springer Science & Business Media, New York (2006)zbMATHGoogle Scholar
  11. 11.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co, Boston (1989)zbMATHGoogle Scholar
  12. 12.
    Guerrero, J., Damian, P., Flores, C., Llamas, P.: Plataforma para gestión de la red de semáforos de zonas urbanas. Sistemas, Cibernética e Informática 1(7), 12–18 (2010)Google Scholar
  13. 13.
    Haklay, M., Weber, P.: OpenStreetMap: user-generated street maps. IEEE Pervasive Comput. 7(4), 12–18 (2008)CrossRefGoogle Scholar
  14. 14.
    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) (2012)Google Scholar
  15. 15.
    Lozano, M., Molina, D., Herrera, F.: Soft Computing: special Issue on scalability of EAs and other metaheuristics for large-scale continuous optimization problems. Technical report (2011)Google Scholar
  16. 16.
    Nebro, A.J., Durillo, J.J., Vergne, M.: Redesigning the jMetal multi-objective optimization framework. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO Companion 2015, pp. 1093–1100. ACM, New York (2015)Google Scholar
  17. 17.
    OMRON Group: Sustainability report 2004. Technical report, Japan (2004)Google Scholar
  18. 18.
    Wen, W.: A dynamic and automatic traffic light control expert system for solving the road congestion problem. Expert Syst. Appl. 34(4), 2370–2381 (2008)CrossRefGoogle Scholar
  19. 19.
    Zhu, Y., Liu, X., Li, M., Zhang, Q.: POVA: traffic light sensing with probe vehicles. IEEE Trans. Parallel Distrib. Syst. 24(7), 1390–1400 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yesnier Bravo
    • 1
    Email author
  • Javier Ferrer
    • 1
  • Gabriel Luque
    • 1
  • Enrique Alba
    • 1
  1. 1.Universidad de MálagaMálagaSpain

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