Cover Image

Coordination Game in Bidirectional Flow

Daichi Yanagisawa


We have introduced evolutionary game dynamics to a one-dimensional cellular- automaton to investigate evolution and maintenance of cooperative avoiding behavior of self-driven particles in bidirectional flow. In our model, there are two kinds of particles, which are right-going particles and left-going particles. They often face opponent particles, so that they swerve to the right or left stochastically in order to avoid conflicts. The particles reinforce their preferences of the swerving direction after their successful avoidance. The preference is also weakened by memory-loss effect.

Result of our simulation indicates that cooperative avoiding behavior is achieved, i.e., swerving directions of the particles are unified, when the density of particles is close to 1/2 and the memory-loss rate is small. Furthermore, when the right-going particles occupy the majority of the system, we observe that their flow increases when the number of left-going particles, which prevent the smooth movement of right-going particles, becomes large. It is also investigated that the critical memory-loss rate of the cooperative avoiding behavior strongly depends on the size of the system. Small system can prolong the cooperative avoiding behavior in wider range of memory-loss rate than large system.


evolutionary game dynamics; cellular automata; bidirectional flow; self-driven particles

Full Text:



Mattli, W., Büthe, T.: Setting International Standards: Technological Rationality or Primacy of Power? World Polit. 56(1), 1-42 (2003). doi:10.1353/wp.2004.0006

Bloomquist, K.M.: Tax Compliance as an Evolutionary Coordination Game: An Agent-Based Approach. Public Financ. Rev. 39(1), 25-49 (2011). doi:10.1177/1091142110381640

Bian, Y.T., Xu, L., Li, J.S.: Evolving dynamics of trading behavior based on coordination game in complex networks. Phys. A Stat. Mech. its Appl. 449, 281-290 (2016). doi:10.1016/j.physa.2015.12.113

Helbing, D.: Traffic and related self-driven many-particle systems. Rev. Mod. Phys. 73(4), 1067-1141 (2001). doi:10.1103/RevModPhys.73.1067

Seyfried, A., Steffen, B., Klingsch, W., Boltes, M.: The fundamental diagram of pedestrian movement revisited. J. Stat. Mech. Theory Exp. 2005(10), P10002-P10002 (2005). doi:10.1088/1742-5468/2005/10/P10002

Antonini, G., Bierlaire, M., Weber, M.: Discrete choice models of pedestrian walking behavior. Transp. Res. Part B Methodol. 40(8), 667-687 (2006). doi:10.1016/j.trb.2005.09.006

Yanagisawa, D., Kimura, A., Tomoeda, A., Nishi, R., Suma, Y., Ohtsuka, K., Nishinari, K.: Introduction of frictional and turning function for pedestrian outflow with an obstacle. Phys. Rev. E 80(3), 036110 (2009). doi:10.1103/PhysRevE.80.036110

Schadschneider, A., Chowdhury, D., Nishinari, K., Santen, L.: Stochastic Transport in Complex Systems. Elsevier, Amsterdam/Oxford (2010)

Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282-4286 (1995). doi:10.1103/PhysRevE.51.4282

Blue, V.J., Adler, J.L.: Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transp. Res. Part B Methodol. 35(3), 293-312 (2001). doi:10.1016/S0191-2615(99)00052-1

Flötteröd, G., Lämmel, G.: Bidirectional pedestrian fundamental diagram. Transp. Res. Part B Methodol. 71(2015), 194-212 (2015). doi:10.1016/j.trb.2014.11.001

Morton, N.A., Hendy, S.C.: Symmetry Breaking in Pedestrian Dynamics (2016). URL

Hoogendoorn, S., Daamen, W.: Self-Organization in Pedestrian Flow. In: Traffic Granul. Flow '03, pp. 373-382. Springer-Verlag Berlin Heidelberg (2005). doi:10.1007/3-540-28091-X_36

Zhang, J., Seyfried, A.: Empirical Characteristics of Different Types of Pedestrian Streams. Procedia Eng. 62, 655-662 (2013). doi:10.1016/j.proeng.2013.08.111

Feliciani, C., Nishinari, K.: Phenomenological description of deadlock formation in pedestrian bidirectional flow based on empirical observation. J. Stat. Mech. Theory Exp. 2015(10), P10003 (2015). doi:10.1088/1742-5468/2015/10/P10003

Konno, T.: Coordination Always Occurs in a Two-Strategy Pure-Coordination Logit Game on Scale-Free Networks. Theor. Econ. Lett. 05(04), 561-570 (2015). doi:10.4236/tel.2015.54066

Hausman, J., McFadden, D.: Specification Tests for the Multinomial Logit Model. Econometrica 52(5), 1219 (1984). doi:10.2307/1910997

Schadschneider, A., Schreckenberg, M.: Cellular automation models and traffic flow. J. Phys. A. Math. Gen. 26(15), L679-L683 (1993). doi:10.1088/0305-4470/26/15/011


Copyright (c) 2016 Daichi Yanagisawa

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.