Timed Cellular Automata-Based Tool for the Analysis of Urban Road Traffic Models

  • Camelia Avram
  • Adina Astilean
  • Eduardo Valente
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 92)


The optimization process of urban transportation in smart cities is strongly connected to the elaboration of specific, efficient models. In this context, this chapter describes a versatile modelling formalism based on timed automata and implemented in the UPPAAL environment for different complex and easily changeable road traffic simulations. Microscopic models based on cellular automatons are analysed in order to simulate the behaviour of different vehicles in a specific group of urban streets. The proposed models integrate the main traffic elements present in urban traffic: streets with multiple traffic lanes; different types of vehicles, including automobiles, buses and trams; intersections controlled by traffic lights; bus and tram stops inside and outside of the traffic lane, pedestrian crosswalks; and parallel street parking. The basic concepts are detailed starting from scenarios which first merit to highlight possible modelling techniques and structures and to facilitate a comparative analysis of the limitations of the presented models. The models based on traffic cellular automata (TCA) have appropriate results inside of the urban traffic theory.


Urban traffic Modelling Simulation Cellular automata Formal verification 


  1. 1.
    Clark J, Daigle G (1997) The importance of simulation techniques in its research and analysis. In: Andradóttir S, Healy KJ, Withers DH, Nelson BL (eds) Proceedings of the 1997 winter simulation conference, USAGoogle Scholar
  2. 2.
    Lima EB (2007) Modelos microscópicos para simulação do tráfego baseados em autômatos celulares. Universidade Federal Fluminense, Niterói, Brazil, Dissertação de Mestrado em ComputaçãoGoogle Scholar
  3. 3.
    May AD (1997) Traffic flow fundamentals. Prentice HallGoogle Scholar
  4. 4.
    Vico FJ, Basagoiti FJ, Platas RG, Lobo D (2005) Modelado y simulación del tráfico en vías urbanas y periurbanas en base a la estimación de tiempos de recorrido. Universidad de Málaga and Tecnologías Viales Aplicadas, TEVA, SL, Malaga, ETSI InformáticaGoogle Scholar
  5. 5.
    Nagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J de Phys I, France, 2221–2229Google Scholar
  6. 6.
    Nakanishi K, Itoh K, Igarashi Y, Bando M (1996) Solvable optimal velocity models and asymptotic trajectory. Department of Physics, Kyoto University, Kyoto 606-01, Physics Division, Dept. of Education, Niigata University, Niigata 950-21, and Physics Division, Aichi University, Miyoshi, Aichi 470-02, 1996, JapanGoogle Scholar
  7. 7.
    Chowdhury D, Santen L, Schadschneider A (2000) Statistical physics of vehicular traffic and some related systems. Phys Rep 329:199–329MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chua L (2005) A nonlinear dynamics perspective of wolfram’s new kind of science. In: Bernoulli shift to universal computation, inaugural lecture of the international Francqui chair, katholieke Universiteit Leuven, June, 2005Google Scholar
  9. 9.
    Murata T (1989) Petri nets: properties, analysis and applications. In: Proceedings of the IEE, vol 77, no 4. Department of Electrical Engineering and Computer Science, University of Illinois, Chicago, USA Apr 1989Google Scholar
  10. 10.
    Maerivoet S, Moor BD (2005) Transportation planning and traffic flow models. 05–155, Katholieke Universiteit Leuven, Department of Electrical Engineering ESAT-SCD (SISTA), July 2005Google Scholar
  11. 11.
    Maerivoet S, Moor BD (2005) Cellular automata models of road traffic. In: Physics reports (ed) Department of Electrical Engineering ESAT-SCD (SISTA), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium, 12 Sept 2005Google Scholar
  12. 12.
    Wolfram S (1983) Statistical mechanics of cellular automata. Rev Mod Phys 55:601–644MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Wolfram S (2002) A new kind of science. Wolfram Media, Inc., ISBN 1-579-955008-8Google Scholar
  14. 14.
    Wang J Petri nets for dynamic event-driven system modeling. Department of Software Engineering, Monmouth University, West Long Branch, USAGoogle Scholar
  15. 15.
    Nagel K (1996) Particle hopping models and traffic flow theory. Phys Rev E 53(5):4655–4672Google Scholar
  16. 16.
    Rothery R (1998) Traffic flow theory. Transportation Research Board (TRB). Special Report, p 165Google Scholar
  17. 17.
    Gardner M (1970) Mathematical games—the fantastic combinations of John Conway’s new solitaire game “life”. Sci Am, 120–123Google Scholar
  18. 18.
    Milner R (1989) Communication and Concurrency. Prentice-Hall International, Englewood CliffsMATHGoogle Scholar
  19. 19.
    Alur R, Dill DL (1994) A theory of timed automata. Theor Comput Sci Elsevier 126:183–235MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Angulo FRF (2014) Autómata celularGoogle Scholar
  21. 21.
    Eppstein D (2014) Cellular automatonGoogle Scholar
  22. 22.
    Vaandrager F A first introduction to Uppaal—a job shop example. Institute for Computing and Information Sciences, Radboud University Nijmegen, Heijendaalseweg 135, 6525 AJ Nijmegen, NetherlandsGoogle Scholar
  23. 23.
    Machado J, Seabra E, Campos JC, Soares F, Leão CP (2011) Safe controllers design for in-dustrial automation systems. Comput Ind Eng 60(4):635–653CrossRefGoogle Scholar
  24. 24.
    Moreira A (2003) Universality and decidability of number-conserving cellular automata. Theor Comput Sci 292:711–721Google Scholar
  25. 25.
    Crutchfield JP, Kaneko K (1987) Phenomenology of spatiotemporal chaos. In: Directions in chaos. World Scientific, pp 272–353Google Scholar
  26. 26.
    Behrmann G, David A, Larsen KG (2004) A tutorial on UPPAAL. In: Proceedings of the 4th international school on formal methods for the design of computer, communication, and software systems (SFM-RT’04). LNCS 3185Google Scholar
  27. 27.
    Kaneko K (1990) Simulating physics with coupled map lattices. In: Kawasaki K, Onuki A, Suzuki M (eds) Formation, dynamics, and statistics of patterns. World Scientific, pp 1–52Google Scholar
  28. 28.
    Kier LB, Seybold PG, Cheng C-K Cellular automata modeling of chemical systems. Published in Springer (ed), Center for the study of biological complexity. Virginia Commonwealth University, Richmond Virginia, USAGoogle Scholar
  29. 29.
    Kunz G, Machado J, Perondi E (2015) Using timed automata for modeling, simulating and verifying networked systems controller’s specifications. Neural Comput. Appl., 1–11Google Scholar
  30. 30.
    Machado J, Denis B, Lesage J-J (2006) A generic approach to build plant models for des verification purposes. In: Proceedings—eighth international workshop on discrete event systems, WODES 2006, pp 407–412Google Scholar
  31. 31.
    Campos AM (2014) Autómato CelularGoogle Scholar
  32. 32.
    Campos JC, Machado J, Seabra E (2008) Property patterns for the formal verification of automated production systems. In: IFAC proceedings volumes (IFAC-PapersOnline), 17 (1 Part 1)Google Scholar
  33. 33.
    Barros C, Leao CP, Soares F, Minas G, Machado J (2013) Issues in remote laboratory developments for biomedical engineering education. In: International conference on interactive collaborative learning, ICL 2013, art. no 6644585, pp 290–295Google Scholar
  34. 34.
    Costa J, Carvalho N, Soares F, Machado J (2009) The fins protocol for complex industrial applications: a case study. In: ICINCO 2009—6th international conference on informatics in control, automation and robotics. Proceedings, 2 RA, pp 348–354Google Scholar
  35. 35.
    Leão CP, Soares FO, Machado JM, Seabra E, Rodrigues H (2011) Design and development of an industrial network laboratory. In: Int J Emerg Technol Learn 6(Special Issue 2):21–26Google Scholar
  36. 36.
    Leão CP, Soares F, Rodrigues H, Seabra E, Machado J, Farinha P, Costa S (2012) Web-assisted laboratory for control education: remote and virtual environments. Communications in Computer and Information Science, 282 CCIS, pp 62–72Google Scholar
  37. 37.
    Silva M, Pereira F, Soares F, Leão CP, Machado J, Carvalho V (2015) An overview of industrial communication networks. Mech Mach Sci 24:933–940CrossRefGoogle Scholar
  38. 38.
    Silva PCM (2001) Teoria do fluxo de tráfego. Universidade de Brasília, Brasil, Material didático do curso de engenharia de tráfegoGoogle Scholar
  39. 39.
    Ceccarelli M, Carbone G, Ottaviano E (2005) Multi criteria optimum design of manipulators. Bull Pol Acad Sci Tech Sci 53(1):9–18MATHGoogle Scholar
  40. 40.
    Thomas F, Ottaviano E, Ros L, Ceccarelli M (2005) Performance analysis of a 3-2-1 pose estimation device. IEEE Trans Rob 21(3):288–297CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Camelia Avram
    • 1
  • Adina Astilean
    • 1
  • Eduardo Valente
    • 2
  1. 1.Technical University of Cluj-NapocaCluj-NapocaRomania
  2. 2.University of MinhoGuimarãesPortugal

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