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The Journal of Supercomputing

, Volume 65, Issue 2, pp 664–681 | Cite as

Cellular automata on FPGA for real-time urban traffic signals control

  • G. Kalogeropoulos
  • G. C. Sirakoulis
  • I. Karafyllidis
Article

Abstract

Among different traffic features, the urban traffic has received a lot of attention due to the ongoing traffic congestion as a result of increased car usage, population growth, and changes in population density. In urban networks, the vehicles flow differs when compared with highways flow because of the freeway’s low speed limit but mostly because of the traffic lights control. In this paper, a real-time hardware implemented bio-inspired model for traffic lights control is presented. The proposed model arrives from Cellular Automata (CAs), which have been proven very flexible and powerful computational traffic models, in that they are able to capture all previously mentioned basic phenomena that occur in traffic flows. The resulting CA model was hardware implemented on FPGA to take full advantage of the inherent parallelism of the CAs and to support the function of an advanced electronic system able to provide real-time adaptive control of traffic lights designed to consider traffic conditions for the whole intersections. The analytical results, obtained by application of the aforementioned FPGA CA processor are found in excellent agreement with the numerical simulations.

Keywords

Traffic signals Cellular automata FPGA Real-time control 

References

  1. 1.
    Avolio MV, Crisci GM, Gregorio SD, Rongo R, Spataro W, Trunfio GA (2006) Sciara [gamma]2: an improved cellular automata model for lava flows and applications to the 2002 Etnean crisis. Comput Geosci 32(7):876–889 CrossRefGoogle Scholar
  2. 2.
    Benjamin SC, Johnson NF, Hui PM (1996) Cellular automata models of traffic flow along a highway containing a junction. J Phys A, Math Gen 29(12):3119 zbMATHCrossRefGoogle Scholar
  3. 3.
    Biham O, Middleton AA, Levine D (1992) Self-organization and a dynamical transition in traffic-flow models. Phys Rev A 46:R6124–R6127 CrossRefGoogle Scholar
  4. 4.
    Brockfeld E, Barlovic R, Schadschneider A, Schreckenberg M (2001) Optimizing traffic lights in a cellular automaton model for city traffic. Phys Rev E 64:056132 CrossRefGoogle Scholar
  5. 5.
    Chowdhury D, Santen L, Schadschneider A (2000) Statistical physics of vehicular traffic and some related systems. Phys Rep 329(4–6):199–329 MathSciNetCrossRefGoogle Scholar
  6. 6.
    Daganzo CF (1995) Requiem for second-order fluid approximations of traffic flow. Transp Res, Part B, Methodol 29(4):277–286 CrossRefGoogle Scholar
  7. 7.
    D’Alotto L (2012) Cellular automata using infinite computations. Appl Math Comput 218(16):8077–8082 MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    D’Ambrosio D, Spataro W (2007) Parallel evolutionary modelling of geological processes. Parallel Comput 33:186–212 MathSciNetCrossRefGoogle Scholar
  9. 9.
    Esser J, Schreckenberg M (1997) Microscopic simulation of urban traffic based on cellular automata. Int J Mod Phys C 08(05):1025–1036 CrossRefGoogle Scholar
  10. 10.
    Feng S, Gu G, Dai S (1997) Effects of traffic lights on ca traffic model. Commun Nonlinear Sci Numer Simul 2(2):70–74 CrossRefGoogle Scholar
  11. 11.
    Georgoudas I, Sirakoulis GC, Scordilis E, Andreadis I (2007) A cellular automaton simulation tool for modelling seismicity in the region of Xanthi. Environ Model Softw 22(10):1455–1464 CrossRefGoogle Scholar
  12. 12.
    Georgoudas I, Kyriakos P, Sirakoulis GC, Andreadis I (2010) An fpga implemented cellular automaton crowd evacuation model inspired by the electrostatic-induced potential fields. Microprocess Microsyst 34(7–8):285–300 CrossRefGoogle Scholar
  13. 13.
    Guan W, He S, Ma J (2012) Review on traffic flow phenomena and theory. J Transp Syst Eng Inf Technol 12(3):90–97 Google Scholar
  14. 14.
    Guoqing G, Poming H, Binghong W, Shiqiang D (1998) Two-dimensional cellular automaton traffic model with randomly switching traffic lights. Appl Math Mech 19:807–813 zbMATHCrossRefGoogle Scholar
  15. 15.
    He S, Guan W (2007) Empirical investigations on traffic phase transitions at Beijing ring road. In: Intelligent transportation systems conference, ITSC 2007. IEEE Press, New York, pp 290–295 Google Scholar
  16. 16.
    Helbing D (1995) Theoretical foundation of macroscopic traffic models. Phys A, Stat Mech Appl 219(3–4):375–390 CrossRefGoogle Scholar
  17. 17.
    Institute TT (2011) Texas Transporation Institute. 2011 urban mobility report. http://mobility.tamu.edu/ums/report/
  18. 18.
    Jendrsczok J, Ediger P, Hoffmann R (2009) A scalable configurable architecture for the massively parallel gca model. Int J Parallel Emerg Distrib Syst 24(7):275–291 MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Kalogeropoulos G, Sirakoulis GC, Karafyllidis I (2011) Fpga implementation of a bioinspired model for real-time traffic signals control. In: Proceedings of the 2011 international conference on scientific computing, CSC 2011. CSREA Press, Las Vegas, pp 290–295 Google Scholar
  20. 20.
    Ke P, Li Y, Nie X (2012) Self-adaptive optimization for traffic flow model based on evolvable hardware. In: Lei J, Wang F, Deng H, Miao D (eds) Artificial intelligence and computational intelligence. Lecture notes in computer science, vol 7530. Springer, Berlin, pp 255–262 CrossRefGoogle Scholar
  21. 21.
    Kerner BS, Konhäuser P (1994) Structure and parameters of clusters in traffic flow. Phys Rev E 50:54–83 CrossRefGoogle Scholar
  22. 22.
    Kotoulas L, Tsarouchis D, Sirakoulis G, Andreadis I (2006) 1-d cellular automaton for pseudorandom number generation and its reconfigurable hardware implementation. In: Proceedings of the 2006 IEEE international symposium on circuits and systems, ISCAS 2006, pp 4627–4630 Google Scholar
  23. 23.
    Li H, Zhang L, Prevedouros PD (2008) Signal control for oversaturated intersections using fuzzy logic. In: Transportation and development innovative best practices 2008. American Society of Civil Engineers, Reston, pp 179–184. Chap 30 Google Scholar
  24. 24.
    Maerivoet S, Moor BD (2005) Cellular automata models of road traffic. Phys Rep 419(1):1–64 MathSciNetCrossRefGoogle Scholar
  25. 25.
    Murtaza S, Hoekstra A, Sloot P (2008) Floating point based cellular automata simulations using a dual fpga-enabled system. In: Second international workshop on high-performance reconfigurable computing technology and applications, HPRCTA 2008, pp 1–8 CrossRefGoogle Scholar
  26. 26.
    Nagel K Schreckenberg M (1992) A cellular automaton model for freeway traffic. J Phys I Fr 2(12):2221–2229 CrossRefGoogle Scholar
  27. 27.
    Nagel K, Wagner P, Woesler R (2003) Still flowing: approaches to traffic flow and traffic jam modeling. Oper Res 51(5):681–710 MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Neumann JV (1966) Theory of self-reproducing automata. University of Illinois Press, Champaign Google Scholar
  29. 29.
    Nishinari K, Fukui M, Schadschneider A (2004) A stochastic cellular automaton model for traffic flow with multiple metastable states. J Phys A, Math Gen 37(9):3101 MathSciNetzbMATHCrossRefGoogle Scholar
  30. 30.
    Prigogine I, Andrews FC (1960) A Boltzmann-like approach for traffic flow. Oper Res 8:789–797 MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Rahman SM, Ratrout NT (2009) Review of the fuzzy logic based approach in traffic signal control: prospects in Saudi Arabia. J Transp Syst Eng Inf Technol 9(5):58–70 Google Scholar
  32. 32.
    Schadschneider A (2000) Statistical physics of traffic flow. Phys A, Stat Mech Appl 285(1–2):101–120 zbMATHCrossRefGoogle Scholar
  33. 33.
    Schadschneider A (2002) Traffic flow: a statistical physics point of view. Phys A, Stat Mech Appl 313(1–2):153–187 MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Schreckenberg M, Schadschneider A, Nagel K, Ito N (1995) Discrete stochastic models for traffic flow. Phys Rev E 51:2939–2949 CrossRefGoogle Scholar
  35. 35.
    Sirakoulis GC (2004) A tcad system for vlsi implementation of the cvd process using vhdl. Integr VLSI J 37(1):63–81 CrossRefGoogle Scholar
  36. 36.
    Sirakoulis GC, Bandini S (eds) (2012) Cellular automata—proceedings of the 10th international conference on cellular automata for research and industry, ACRI 2012, Santorini Island, Greece, 24–27 September 2012. Lecture notes in computer science, vol 7495. Springer, Berlin Google Scholar
  37. 37.
    Sirakoulis GC, Karafyllidis I, Mardiris V, Thanailakis A (1999) Study of lithography profiles developed on non-planar Si surfaces. Nanotechnology 10(4):421 CrossRefGoogle Scholar
  38. 38.
    Sirakoulis GC, Karafyllidis I, Thanailakis A (2003) A cad system for the construction and vlsi implementation of cellular automata algorithms using vhdl. Microprocess Microsyst 27(8):381–396 CrossRefGoogle Scholar
  39. 39.
    Sirakoulis GC, Karafyllidis I, Spataro W (2009) A computational intelligent oxidation process model and its vlsi implementation. In: Proceedings of the 2009 international conference on scientific computing, CSC 2009, 13–16 July 2009, CSREA Press, Las Vegas, pp 329–335 Google Scholar
  40. 40.
    Spataro W, Avolio MV, Lupiano V, Trunfio GA, Rongo R, D’Ambrosio D (2010) The latest release of the lava flows simulation model sciara: first application to mt etna (Italy) and solution of the anisotropic flow direction problem on an ideal surface. Proc Comput Sci 1(1):17–26 CrossRefGoogle Scholar
  41. 41.
    Spezzano G, Talia D, Gregorio SD, Rongo R, Spataro W (1996) A parallel cellular tool for interactive modeling and simulation. Comput Sci Eng 3:33–43 CrossRefGoogle Scholar
  42. 42.
    Szklarski J (2010) Cellular automata model of self-organizing traffic control in urban networks. Bull Pol Acad Sci, Tech Sci 58(3):435–441 Google Scholar
  43. 43.
    Takayasu M, Takayasu H (1993) 1/f noise in a traffic model. Fractals 1(4):860–866 zbMATHCrossRefGoogle Scholar
  44. 44.
    Toffoli T (1984) Cam: a high-performance cellular-automaton machine. Phys D, Nonlinear Phenom 10(1–2):195–204 MathSciNetCrossRefGoogle Scholar
  45. 45.
    Tripp JL, Mortveit HS, Gokhale M (2004) Acceleration of traffic simulation on reconfigurable hardware. In: Proceedings of the 2004 MAPLD international conference Google Scholar
  46. 46.
    Varas A, Cornejo MD, Toledo BA, Muñoz V, Rogan J, Zarama R, Valdivia JA (2009) Resonance, criticality, and emergence in city traffic investigated in cellular automaton models. Phys Rev E 80:056108 CrossRefGoogle Scholar
  47. 47.
    Wei J, Wang A, Du N (2005) Study of self-organizing control of traffic signals in an urban network based on cellular automata. IEEE Trans Veh Technol 54(2):744–748 CrossRefGoogle Scholar
  48. 48.
    Wilding N, Trew A, Hawick K, Pawley G (1991) Scientific modeling with massively parallel simd computers. Proc IEEE 79(4):574–585 CrossRefGoogle Scholar
  49. 49.
    Wolf DE (1999) Cellular automata for traffic simulations. Phys A, Stat Mech Appl 263(1–4):438–451 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • G. Kalogeropoulos
    • 1
  • G. C. Sirakoulis
    • 1
  • I. Karafyllidis
    • 1
  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece

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