Comparative Analysis of Chosen Adaptive Traffic Control Algorithms

  • Krzysztof MałeckiEmail author
  • Piotr Pietruszka
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 21)


Setting optimal scenario for traffic lights on crossroads is very important task related to modeling of modern, ordered traffic in smart cities. In this article modifications of traffic lights phases control algorithms on crossroads with different densities have been presented. Comparative analysis of chosen algorithms effectiveness for defined area has been also made. Particularly strategies based on traffic detectors placed in front or behind a crossroad and algorithm “injecting” cars have been compared. The second solution is dedicated to situation with autonomous (driverless) vehicles working with high time accuracy. Solutions developed using traffic simulation allowed to prove that proposed modifications of traffic lights control algorithms can improve effectiveness in specified cases.


Intelligent Transportation systems Traffic efficiency Traffic simulators Adaptive traffic signal control 


  1. 1.
    Taniguchi, E., Shimamoto, H.: Intelligent transportation system based dynamic vehicle routing and scheduling with variable travel times. Transp. Res. Part C: Emerg. Technol. 12(3), 235–250 (2004)CrossRefGoogle Scholar
  2. 2.
    Feng, Y.H., Head, K.L., Khoshmagham, S., Zamanipour, M.: A real-time adaptive signal control in a connected vehicle environment. Transp. Res. Part C 55, 460–473 (2015)CrossRefGoogle Scholar
  3. 3.
    Luyanda, F., Gettman, D., Head, L., Shelby, S., Bullock, D., Mirchandani, P.: ACS-lite algorithmic architecture: applying adaptive control system technology to closed-loop traffic signal control systems. Transp. Res. Rec. 1856, 175–184 (2003)CrossRefGoogle Scholar
  4. 4.
    Sims, A.G., Dobinson, K.W.: The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits. IEEE Trans. Veh. Technol. 29(2), 130–137 (1980)CrossRefGoogle Scholar
  5. 5.
    Hunt, P.B., Robertson, D.I., Bretherton, R.D., Royle, M.C.: The SCOOT on-line traffic signal optimisation technique. Traffic Eng. Control 23(4), 190–199 (1982)Google Scholar
  6. 6.
    Gartner, N.: OPAC: a demand-responsive strategy for traffic signal control. Transp. Res. Rec. 906, 75–81 (1983)Google Scholar
  7. 7.
    Mauro, V., Taranto, C.D.: UTOPIA. IFAC Control, France (1989)Google Scholar
  8. 8.
    Roozemond, D.A.: Using intelligent agents for pro-active, real-time urban intersection control. Eur. J. Oper. Res. 131(2), 293–301 (2001)CrossRefzbMATHGoogle 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 2014, pp. 586–591. IEEE Press, Sydney (2014)Google Scholar
  10. 10.
    Kanungo, A., Sharma, A., Singla, C.: Smart traffic lights switching and traffic density calculation using video processing. In: Proceedings of 2014 Recent Advances in Engineering and Computational Sciences, pp. 1–7. IEEE Press, Chandigarh (2014)Google Scholar
  11. 11.
    De Oliveira, M.B.W., De Almeida, N.A.: Optimization of traffic lights timing based on artificial neural networks. In: 17th IEEE International Conference on Intelligent Transportation Systems, pp. 1921–1922. IEEE Press, Qingdao (2014)Google Scholar
  12. 12.
    Goel, S., Bush, S.F., Ravindranathan, K.: Self-organization of traffic lights for minimizing vehicle delay. In: International Conference on Connected Vehicles and Expo, pp. 931–936. IEEE Press, Vienna (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, pp. 1–7. IEEE Press, Istambul (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, pp. 2810–2815. IEEE Press, Osaka (2016)Google Scholar
  15. 15.
    Qi, L., Zhou, M., Luan, W.: An Emergency Traffic Light Strategy to Prevent Traffic Congestion. 13th IEEE International Conference on Networking. Sensing and Control, pp. 1–6. IEEE Press, Mexico City (2016)Google Scholar
  16. 16.
    Ruchaj, M., Stanisławski, R. Approaches to control acyclic traffic lights in an exemplary urban road network. Methods and models in automation and robotics. In: Proceedings of the 16th International Conference on Methods and Models in Automation and Robotics, pp. 387–392. IEEE Press, Międzyzdroje (2011)Google Scholar
  17. 17.
    Oskarbski, J., Gumińska, L., Miszewski, M., et al.: Analysis of Signalized Intersections in the Context of Pedestrian Traffic. Transp. Res. Proc. 14, 2138–2147 (2016)CrossRefGoogle Scholar
  18. 18.
    Oskarbski, J., Miszewski, M., Żarski, K.: Traffic control within bus queue jumps on the example of Gdynia. Transp. Miejski i Regionalny 4, 38–43 (2015)Google Scholar
  19. 19.
    Bazan, M., Ciskowski, P., Halawa, K., Janiczek, T., Kozaczewski, P., Madej, Ł., Rusiecki, A.: Intelligent transport system auditing using road traffic micro-simulation. Arch. Transp. Syst. Telemat. 8(4), 3–8 (2015)Google Scholar
  20. 20.
    Bazan, M., Ciskowski, P., Janiczek, T., Madej, Ł., Rusiecki, A., Halawa, K.: Green wave optimization. Arch. Transp. Syst. Telemat. 9(3), 3–8 (2015)Google Scholar
  21. 21.
    Karoń, G., Mikulski, J.: Transportation systems modelling as planning, organisation and management for solutions created with ITS. In: Mikulski, J. (ed.) Modern Transport Telematics. CCIS, vol. 239, pp. 277–290. Springer, Berlin, Heidelberg (2011)CrossRefGoogle Scholar
  22. 22.
    Karoń, G., Mikulski, J.: Problems of ITS architecture development and ITS architecture implementation in Upper-Silesian conurbation in Poland. In: Mikulski, J. (ed.) Telematics in the Transport Environment. CCIS, vol. 329, pp. 183–198. Springer, Berlin, Heidelberg (2012)CrossRefGoogle Scholar
  23. 23.
    Karoń, G., Żochowska, R.: Modelling of expected traffic smoothness in urban transportation systems for ITS solutions. Arch. Transp. 33(1), 33–45 (2015)CrossRefGoogle Scholar
  24. 24.
    Żochowska, R., Karoń, G.: ITS Services packages as a tool for managing traffic congestion in cities. In: Sładkowski, A., Pamuła, W. (eds.) Intelligent Transportation Systems-Problems and Perspectives. SSDC, vol. 32, pp. 81–103. Springer, Switzerland (2016)CrossRefGoogle Scholar
  25. 25.
    Sobota, A., Kłos, M., Karoń, G.: The influence of countdown timers on the traffic safety of pedestrians and vehicles at the signalized intersection. In: Sierpiński, G. (ed.) Intelligent Transport Systems and Travel Behaviour. AISC, vol. 505, pp. 13–21. Springer, Switzerland (2017)CrossRefGoogle Scholar
  26. 26.
    Wnuk, D., Karoń, G.: Variants to improve traffic conditions in the bottleneck in road network of the Upper-Silesian conurbation. In: VI International Scientific Conference Transport Problems Conference Proceedings on CD-ROM, pp. 970–981. Silesian University of Technology, Katowice (2014)Google Scholar
  27. 27.
    Mikat, J., Brockfeld, E., Wagner, P.: Agent based traffic signals on a basic grid. In: Proceedings of the 4th Workshop on Agent-Based Simulation, pp. 1–6. SCS European Publishing House, Erlangen (2003)Google Scholar
  28. 28.
    Małecki, K., Pietruszka, P., Iwan, S.: Selected aspects of traffic micro-simulation based on cellular automata and traffic detection system. Arch. Transp. Syst. Telemat. 6(1), 41–44 (2013)Google Scholar
  29. 29.
    Małecki, K.: The importance of automatic traffic lights time algorithms to reduce the negative impact of transport on the urban environment. Transp. Res. Proc. 16, 329–342 (2016)CrossRefGoogle Scholar
  30. 30.
    Krajzewicz, D., Hertkorn, G., Rössel, C., Wagner, P.: SUMO (Simulation of Urban MObility) - an open-source traffic simulation. In: Proceedings of the 4th Middle East Symposium on Simulation and Modelling, pp. 183–187. SCS European Publishing House, Erlangen (2002)Google Scholar
  31. 31.
    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
  32. 32.
    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
  33. 33.
    Gu, W., Ito, T.: Optimization of road distribution for traffic system based on vehicle’s priority. In: Booth, R., Zhang, M. (eds.) PRICAI 2016: Trends in Artificial Intelligence. LNCS, vol. 9810, pp. 729–737. Springer, Switzerland (2016)CrossRefGoogle Scholar
  34. 34.
    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 16(5), 1–26 (2016)CrossRefGoogle Scholar
  35. 35.
  36. 36.
    Małecki, K., Pietruszka, P., Iwan, S.: Comparative analysis of selected algorithms in the process of optimization of traffic lights. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) Intelligent Information and Database Systems. LNAI, vol. 10192, pp. 497–506. Springer, Berlin (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Computer ScienceWest Pomeranian University of TechnologySzczecinPoland

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