A Cellular Automaton Based System for Traffic Analyses on the Roundabout

  • Krzysztof Małecki
  • Jarosław Wątróbski
  • Waldemar Wolski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)

Abstract

The paper presents an analysis of the impact of road conditions, the distance between vehicles and the number of pedestrians on the roundabout capacity. The study was based on a developed cellular automaton (CA) model and the implemented simulation system. The developed CA model extends the basic traffic model with a braking mechanism. It also reflects the actual technical conditions of vehicles (acceleration and braking depending on the dimensions and functions of the vehicle, as well as the driving at a roundabout of different speeds that are appropriate for the size of the vehicle). The study was based on the example of a two-lane roundabout with four two-lane inlet roads.

Keywords

Capacity of roundabout Cellular automaton (CA) Roundabout traffic simulation Weather conditions Pedestrians Distance between vehicles 

References

  1. 1.
  2. 2.
    Transportation Research Board of the National Acad: National Cooperative Highway Research Program Report 572 - Roundabouts in the Unites States (2007)Google Scholar
  3. 3.
    Leaf, W.A., Preusser, D.F.: Literature review on vehicle travel speeds and pedestrian injuries, US Department of Transportation, National Highway Traffic Safety Administration (1999)Google Scholar
  4. 4.
    Brude, U., Larsson, J.: What roundabout design provides the highest possible safety? Nordic Road Transp. Res. 12(2), 17–21 (2000)Google Scholar
  5. 5.
    Macioszek, E.: Relationship between vehicle stream in the circular roadway of a one-lane roundabout and traffic volume on the roundabout at peak hour. In: Mikulski, J. (ed.) TST 2014. CCIS, vol. 471, pp. 110–119. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45317-9_12 Google Scholar
  6. 6.
    Macioszek, E., Sierpiński, G., Czapkowski, L.: Problems and issues with running the cycle traffic through the roundabouts. In: Mikulski, J. (ed.) TST 2010. CCIS, vol. 104, pp. 107–114. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16472-9_11 CrossRefGoogle Scholar
  7. 7.
    Macioszek, E.: Analysis of significance of differences between psychotechnical parameters for drivers at the entries to one-lane and turbo roundabouts in Poland. In: Sierpiński, G. (ed.) Intelligent Transport Systems and Travel Behaviour. AISC, vol. 505, pp. 149–161. Springer, Cham (2017). doi:10.1007/978-3-319-43991-4_13 CrossRefGoogle Scholar
  8. 8.
    Wang, R., Liu, M.: A realistic cellular automata model to simulate traffic flow at urban roundabouts. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2005. LNCS, vol. 3515, pp. 420–427. Springer, Heidelberg (2005). doi:10.1007/11428848_56 CrossRefGoogle Scholar
  9. 9.
    Nagel, K., Schreckenberg, M.: A cellular automata model for freeway traffic. J. de Phys. I 2, 2221–2229 (1992)Google Scholar
  10. 10.
    Chowdhury, D., Santen, L., Schadschneider, A.: Statistical physics of vehicular traffic and some related systems. Phys. Rep. 329, 199–329 (2000)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Fellendorf, M.: VISSIM: a microscopic simulation tool to evaluate actuated signal control including bus priority. In: 64th Institute Transportation Engineers (ITE) Annual Meeting, Technical paper, Session 32, Dallas, TX, pp. 1–9 (1994)Google Scholar
  12. 12.
    Barcelo, J., Ferrer, J.L., Montero, L.: AIMSUN: Advanced Interactive Microscopic Simulator for Urban Networks, User‘s Manual, Departament d ‘Estadística i Investigació Operativa, UPC (1997)Google Scholar
  13. 13.
    Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO-simulation of urban mobility. Int. J. Adv. Syst. Meas. 5, 128–138 (2012)Google Scholar
  14. 14.
    Popescu, M.C., Ranea, C., Grigoriu, M.: Solutions for traffic lights intersections control. In: Proceedings of the 10th WSEAS (2010)Google Scholar
  15. 15.
    Han, X., Sun, H.: The implementation of traffic signal light controlled by PLC. J. Chang. Inst. Opt. Fine Mech. 4, 029 (2003)Google Scholar
  16. 16.
    Kołopieńczyk, M., Andrzejewski, G., Zając, W.: Block programming technique in traffic control. In: Mikulski, J. (ed.) TST 2013. CCIS, vol. 395, pp. 75–80. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41647-7_10 CrossRefGoogle Scholar
  17. 17.
    Jaszczak, S., Małecki, K.: Hardware and software synthesis of exemplary crossroads in a modular programmable controller. Prz. Elektrotech. 89(11), 121–124 (2013)Google Scholar
  18. 18.
    Sierpiński, G.: Theoretical model and activities to change the modal split of traffic. In: Mikulski, J. (ed.) TST 2012. CCIS, vol. 329, pp. 45–51. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34050-5_6 CrossRefGoogle Scholar
  19. 19.
    Sierpiński, G.: Travel behaviour and alternative modes of transportation. In: Mikulski, J. (ed.) TST 2011. CCIS, vol. 239, pp. 86–93. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24660-9_10 CrossRefGoogle Scholar
  20. 20.
    Karoń, G., Mikulski, J.: Transportation systems modelling as planning, organisation and management for solutions created with ITS. In: Mikulski, J. (ed.) TST 2011. CCIS, vol. 239, pp. 277–290. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24660-9_32 CrossRefGoogle Scholar
  21. 21.
    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.) ACIIDS 2017. LNCS, vol. 10192, pp. 497–506. Springer, Cham (2017). doi:10.1007/978-3-319-54430-4_48 CrossRefGoogle Scholar
  22. 22.
    Webpage: http://traffic-simulation.de. Accessed Dec 2016
  23. 23.
    Nagel, K., Wolf, D.E., Wagner, P., Simon, P.M.: Two-lane traffic rules for cellular automata: a systematic approach. Phys. Rev. E 58(2), 1425–1437 (1998)CrossRefGoogle Scholar
  24. 24.
    Biham, O., Middleton, A.A., Levine, D.: Phys. Rev. A 46, 6124 (1992)CrossRefGoogle Scholar
  25. 25.
    Chowdhury, D., Schadschneider, A.: Self-organization of traffic jams in cities: effects of stochastic dynamics and signal periods. Phys. Rev. E 59, 1311–1314 (1999)CrossRefGoogle Scholar
  26. 26.
    Małecki, K., Iwan, S.: Development of cellular automata for simulation of the crossroads model with a traffic detection system. In: Mikulski, J. (ed.) TST 2012. CCIS, vol. 329, pp. 276–283. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34050-5_31 CrossRefGoogle Scholar
  27. 27.
    Hartman, D.: Head leading algorithm for urban traffic modelling. Positions 2, 1 (2004)Google Scholar
  28. 28.
    Gwizdałła, T.M., Grzebielucha, S.: The traffic flow through different form of intersections. In: International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 299–304. IEEE (2010)Google Scholar
  29. 29.
    Belz, N.P., Aultman-Hall, L., Montague, J.: Influence of priority taking and abstaining at single-lane roundabouts using cellular automata. Transp. Res. Part C: Emerg. Technol. 69, 134–149 (2016)CrossRefGoogle Scholar
  30. 30.
    Wang, R., Ruskin, H.: Modeling traffic flow at a single-lane urban roundabout. Comput. Phys. Commun. 147, 570–576 (2002)CrossRefMATHGoogle Scholar
  31. 31.
    Lakouari, N., Ez-Zahraouy, H., Benyoussef, A.: Traffic flow behaviour at a single lane roundabout as compared to traffic circle. Phys. Lett. Sect. A: Gen. At. Solid State Phys. 378(43), 3169–3176 (2014)CrossRefMATHGoogle Scholar
  32. 32.
    Belz, N.P., Aultman-Hall, L., Lee, B.H.Y., Gårder, P.E.: An event-based framework for non-compliant driver behavior at single-lane roundabouts. Transp. Res. Rec.: J. Transp. Res. Board Nat. Acad. 2402, 38–46 (2014). Washington, DCCrossRefGoogle Scholar
  33. 33.
    Wagner, P., Nagel, K., Wolf, D.: Realistic multilane traffic rule for cellular automata. Phys. A 234, 687–698 (1997)CrossRefGoogle Scholar
  34. 34.
    Wang, R., Ruskin, H.J.: Modelling traffic flow at multi-lane urban roundabouts. Int. J. Mod. Phys. C 17(5), 693–710 (2006)CrossRefMATHGoogle Scholar
  35. 35.
    Schroeder, B., Rouphail, N., Salamati, K., Bugg, Z.: Effect of pedestrian impedance on vehicular capacity at multilane roundabouts with consideration of crossing treatments. Transp. Res. Rec.: J. Transp. Res. Board Nat. Acad. 2312(10), 14–24 (2012)CrossRefGoogle Scholar
  36. 36.
    Was, J.: Cellular automata model of pedestrian dynamics for normal and evacuation conditions. In: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, ISDA 2005, pp. 154–159. IEEE (2005)Google Scholar
  37. 37.
    Wąs, J., Gudowski, B., Matuszyk, P.J.: New cellular automata model of pedestrian representation. In: El Yacoubi, S., Chopard, B., Bandini, S. (eds.) ACRI 2006. LNCS, vol. 4173, pp. 724–727. Springer, Heidelberg (2006). doi:10.1007/11861201_88 CrossRefGoogle Scholar
  38. 38.
  39. 39.
    Bułka, D., Walczak, S., Wolak, S.: Braking process - legal and technical aspects in terms of simulation and analysis. In: Proceedings of the 3rd Conference on Rozwój techniki samochodowej a ubezpieczenia komunikacyjne (2006). (in Polish)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Krzysztof Małecki
    • 1
  • Jarosław Wątróbski
    • 1
  • Waldemar Wolski
    • 2
  1. 1.West Pomeranian University of TechnologySzczecinPoland
  2. 2.University of SzczecinSzczecinPoland

Personalised recommendations