By introducing toxicity coefficient to the static force field and pheromone model to the dynamic force field, an improved cellular automata evacuation model (ICAEM) is developed. Evacuation experiments in an 11 m × 8 m classroom are carried out, the time from their initial position to the single exit or double exits of the classroom and the stagnation time near the exits are recorded and compared with the simulation results to validate the ICAEM model, to check the influencing factors of the exit position KS, the repulsive coefficient KR, and the pheromone damping coefficient ρ in the model. The results show that when the pheromone damping coefficient ρ = 0.5, the self-lining phenomenon is significant. If KS = 1, KR = 0.12, the simulation result is very consistent with the actual evacuation time in the experimental tests. Finally, the ICAEM model is applied in a 118 m × 36 m twenty-first-century subway station platform where there are a total of 2500 persons from two trains waiting for evacuation. The simulation error is reasonable comparing with the calculation results by engineering design code of PRC and the Togawa’s formula. It is found that the original layout of EXIT 2 obstructed the evacuation flow of the station platform at a certain degree, resulting in bidirectional counter flow and stagnation near the exit. After improving the layout of the EXIT 2 on the platform based on the simulation results by ICAEM, the RSET of the whole platform is shortened and the stagnation phenomenon is attenuated. The ICAEM model is applicable and meaningful to the crowd evacuation and performance-based safety design in high densely populated public places.
Crowd evacuation behavior Cellular automata Pheromone Following phenomenon Required safety evacuation time
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This paper was supported by the National Key R&D Program of China (2017YFC0804900, 2017YFC0804906)
Pauls, J. (1984). The movement of people in buildings and design solutions for means of egress. Fire Technology,20(1), 27–47.CrossRefGoogle Scholar
Fruin, J. J. (1993). “The causes and prevention of crowd disasters”, Originally presented at The First International Conference on Engineering for Crowd Safety, London, England (Revised exclusively for crowdsafe.com, January 2002.) Available at http://www.crowdsafe.com/FruinCauses.pdf. Accessed September 19, 2017.
Zhen, W., Mao, L., & Yuan, Z. (2008). Analysis of trample disaster and a case study—Mihong bridge fatality in China in 2004. Safety Science,46, 1255–1270.CrossRefGoogle Scholar
Alnabulsia, H., & Drurya, J. (2014). Social identification moderates the effect of crowd density on safety at the Hajj. PNAS,111(25), 9091–9096.CrossRefGoogle Scholar
Valach, L., Young, R. A., & Lynam, M. J. (2002). A primer for applied research in the social sciences. Westport, Connecticut: Praeger Publishers.Google Scholar
Lee, R. S. C., & Hughes, R. L. (2007). Minimisation of the risk of trampling in a crowd. Mathematics and Computers in Simulation,74, 29–37.MathSciNetCrossRefGoogle Scholar
Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review E,51, 4282–4286.CrossRefGoogle Scholar
Helbing, D., Farkas, I., & Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature,407(28), 487–490.CrossRefGoogle Scholar
Burstedde, C., Klauck, K., Schadschneider, A., et al. (2001). Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A,295(3–4), 507–525.CrossRefGoogle Scholar
Kirchner, A., & Schadschneider, A. (2002). Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Physica A: Statistical Mechanics and its Applications,312(1–2), 260–276.CrossRefGoogle Scholar
Yang, L. Z., Fang, W. F., & Fan, W. C. (2003). Modeling occupant evacuation using cellular automata-effect of human behavior and building characteristics on evacuation. Journal of Fire Science,21(3), 227–240.CrossRefGoogle Scholar
Zhao, D. L., Yang, L. Z., & Li, J. (2008). Occupants’ behavior of going with the crowd based on cellular automata occupant evacuation model. Physica A,387, 3708–3718.CrossRefGoogle Scholar
Alizadeh, R. (2011). A dynamic cellular automaton model for evacuation process with obstacles. Safety Science,49, 315–323.CrossRefGoogle Scholar
Fu, Z., Yang, L., Chen, Y., Zhu, Z., et al. (2013). The effect of individual tendency on crowd evacuation efficiency under inhomogeneous exit attraction using a static field modified FFCA model. Physica A, 392, 6090–6099.CrossRefGoogle Scholar
Dorigo, M., Birattari, M., & Stützle, T. (2006). Ant colony optimization-artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine,285(34), 1302–1321.Google Scholar
Zhao, B., Li, S., & Jin, J. (2007). Ant colony algorithm based on adaptive selection of paths and pheromone updating. Computer Engineering and Applications,43(3), 12–15.Google Scholar
Liu, M., Zhang, F., Ma, Y., et al. (2016). Evacuation path optimization based on quantum ant colony algorithm. Advanced Engineering Informatics,30, 259–267.CrossRefGoogle Scholar
Forcael, E., Gonzalez, V., Orozco, F., et al. (2014). Ant colony optimization model for tsunamis evacuation routes. Computer-Aided Civil and Infrastructure Engineering,29, 723–737.CrossRefGoogle Scholar