Cellular automata model of self-organizing traffic control in urban networks

• Autorzy: Szklarski J.
• Języki publikacji: EN

Abstrakty

EN

A model of city traffic based on Nagel-Schreckenberg cellular automaton (CA) model is presented. Traffic control is realized at intersections with two conflicting streams each (at any time at most one stream can have “green light” assigned to it). For simple and regular lattice-like networks which are considered, it is easy to find optimal switching periods giving maximum possible flow rates. These optimal strategies are compared with a self-controlling approach proposed by [1], which has not been implemented in a CA model until now. Previous work proved that generally this method gives superior results when compared to classical methods. In this paper we show that for deterministic scenario such control leads to self-organization, and that the solution always quickly converges to the optimal solution which is known in this case. Moreover, we consider also non-deterministic case, in the sense that possibility of turning with given probability is allowed. It is shown that the self-controlling strategy always gives better results than any solution based on fixed cycles with green waves.

Słowa kluczowe

EN

cellular automata model; self-organizing traffic control; urban networks

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2010
• Tom: Vol. 58, nr 3
• Strony: 435--441
• Opis fizyczny: Bibliogr. 13 poz., rys.

Twórcy

autor

Szklarski J.

• Institute of Fundamental Technological Research, Polish Academy of Sciences, 5B Pawińskiego St., 02-106 Warszawa, Poland,

Bibliografia

• [1] S. Lammer and D. Helbing, “Self-control of traffic lights and vehicle flows in urban road networks”, J. Statistical Mechanics Theory and Experiment 4, P04019 (2008).
• [2] A. Warberg, J. Larsen, and R.M. Jørgensen, “Green wave traffic optimization – a survey”, Technical Report, Informatics and Mathematical Modelling 1, CD-ROM (2008).
• [3] D. Helbing, “Traffic and related self-driven many-particle systems”, Reviews of Modern Physics 73 (4), 1067 (2001).
• [4] C.H. Papadimitriou and J.N. Tsitsiklis, “The complexity of optimal queuing network control”, Math. Oper. Res. 24, 293 (1999).
• [5] D. Helbing, S. Lammer, and J.-P. Lebacque, “Self-organized control of irregular or perturbed network traffic”, 239 (2005).
• [6] C. Gershenson, “Self-organizing traffic lights”, Complex Systems 16, 29 (2004).
• [7] S. L¨ammer, R. Donner, and D. Helbing, “Anticipative control of switched queueing systems”, Eur. Physical J. B 63 (3), 341 (2007).
• [8] E. Brockfeld, R. Barlovic, A. Schadschneider, and M. Schreckenberg, “Optimizing traffic lights in a cellular automaton model for city traffic”, Physical Review E 64 (5), 056132 (2001).
• [9] D. Chowdhury, L. Santen, and A. Schadschneider, “Statistical physics of vehicular traffic and some related systems”, Physics Reports 329 (4–6), 199–329 (2000).
• [10] K. Nagel and M. Schreckenberg, “A cellular automaton model for freeway traffic”, J. de Physique I 2 (12), 2221–2229 (1992).
• [11] K. Nagel and M. Paczuski, “Emergent traffic jams”, Phys. Rev. E 51 (4), 2909–2918 (1995).
• [12] M. Takayasu and H. Takayasu, Fractals 1 (4), 860 (1993).
• [13] S. L¨ammer, J. Krimmling, and A. Hoppe, “Selbst-steuerung von lichtsignalanlagen – regelungstechnischer ansatz und simulation”, Straßenverkehrstechnik 11, 714–721 (2009).