American Institute of Mathematical Sciences

Advanced
September  2015, 10(3): 631-646. doi: 10.3934/nhm.2015.10.631

A particle swarm optimization model of emergency airplane evacuations with emotion

Junyuan Lin 1, and  Timothy A. Lucas 1,

1. 

Pepperdine University, 24255 Pacific Coast Highway, Malibu, CA 90263, United States, United States

Received  November 2014 Revised  April 2015 Published  July 2015

Recent incidents such as the Asiana Flight 214 crash in San Francisco on July 6, 2013 have brought attention to the need for safer aircraft evacuation plans. In this paper we propose an emergency aircraft evacuation model inspired by Particle Swarm Optimization (PSO). By introducing an attraction-replusion force from swarm modeling we considered realistic behaviors such as feeling push-back from physical obstacles as well as reducing gaps between passengers near emergency exits. We also incorporate a scaled emotion quantity to simulate passengers experiencing fear or panic. In our model elevating emotion increases the velocity of most passengers and decreases the effect of forces exerted by nearby passengers. We also allow a small percentage of passengers to experience a sense of panic that slows their motion. Our first simulations model a Boeing 737-800 with a single class of seats that are distributed uniformly throughout the aircraft. We also simulate the evacuation of a Boeing 777-200ER with multiple service classes. We observed that increasing emotion causes most passengers to move more quickly to the exits, but that passengers experiencing panic can slow down the evacuation. Our simulations also suggest that blocking exits in locations with high seat density significantly delays the evacuation.
Keywords: airplane evacuation., contagion, Particle swarm optimization.
Mathematics Subject Classification: Primary: 68T42, 90C90; Secondary: 68T05, 93C5.
Citation: Junyuan Lin, Timothy A. Lucas. A particle swarm optimization model of emergency airplane evacuations with emotion. Networks & Heterogeneous Media, 2015, 10 (3) : 631-646. doi: 10.3934/nhm.2015.10.631
References:
[1]

Y. Chuang, M. R. D'orsogna, D. C. Marthaler, A. L. Bertozzi and L. S. Chayes, State transitions and the continuum limit for a 2D interacting, self-propelled particle system,, Physica D, 232 (2007), 33.  doi: 10.1016/j.physd.2007.05.007.  Google Scholar

[2]

T. J. Cova and J. P. Johnson, A network flow model for lane-based evacuation routing,, Transportation Research Part A: Policy and Practice, 37 (2003), 579.  doi: 10.1016/S0965-8564(03)00007-7.  Google Scholar

[3]

F. Cucker and S. Smale, Emergent behavior in flocks,, IEEE Transactions on Automatic Control, 52 (2007), 852.  doi: 10.1109/TAC.2007.895842.  Google Scholar

[4]

K. Depart, et al., Aircraft evacuation testing: Research and technology issues,, Office of Technology Assessment, (): 1.   Google Scholar

[5]

R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory,, in Proceedings of the Sixth International Symposium on Micro Machine and Human Science, (1995), 39.  doi: 10.1109/MHS.1995.494215.  Google Scholar

[6]

E. Galea and J. P. Galparsoro, A computer-based simulation model for the prediction of evacuation from mass-transport vehicles,, Fire Safety Journal, 22 (1994), 341.  doi: 10.1016/0379-7112(94)90040-X.  Google Scholar

[7]

E. Galea and J. Galparsoro, Exodus: An Evacuation Model for Mass Transport Vehicles,, Papers, (1993).   Google Scholar

[8]

J. Garner, R. F. Chandler and E. Cook, GPSS Computer Simulation of Aircraft Passenger Emergency Evacuations,, U.S. Department of Transportation, (1978).   Google Scholar

[9]

R. Hassan, B. Cohanim, O. De Weck and G. Venter, A comparison of particle swarm optimization and the genetic algorithm,, in 46th AIAA/ASME/ASCE/AHS/ASC Structures, (2005), 1.  doi: 10.2514/6.2005-1897.  Google Scholar

[10]

D. Helbing, I. Farkas, P. Molnàr and T. Vicsek, Simulation of pedestrian crowds in normal and evacuation situations,, in Pedestrian and Evacuation Dynamics (eds. M. Schreckenberg and S. D. Sharma), (2002), 21.   Google Scholar

[11]

J. Izquierdo, I. Montalvo, R. Pérez and V. Fuertes, Forecasting pedestrian evacuation times by using swarm intelligence,, Physica A: Statistical Mechanics and its Applications, 388 (2009), 1213.  doi: 10.1016/j.physa.2008.12.008.  Google Scholar

[12]

P. Jorna, et al., Increasing the survival rate in aircraft accidents: Impact protection, fire survivability and evacuation,, European Transport Safety Council, (): 1.   Google Scholar

[13]

Y. Liu, W. Wang, H.-Z. Huang, Y. Li and Y. Yang, A new simulation model for assessing aircraft emergency evacuation considering passenger physical characteristics,, Reliability Engineering & System Safety, 121 (2014), 187.  doi: 10.1016/j.ress.2013.09.001.  Google Scholar

[14]

T. A. Lucas, Operator splitting for an immunology model using reaction-diffusion equations with stochastic source terms,, SIAM J. Numer. Anal., 46 (2008), 3113.  doi: 10.1137/070701595.  Google Scholar

[15]

T. A. Lucas, Maximum-norm estimates for an immunology model using reaction-diffusion equations with stochastic source terms,, SIAM J. Numer. Anal., 49 (2011), 2256.  doi: 10.1137/100794584.  Google Scholar

[16]

T. Miyoshi, H. Nakayasu, Y. Ueno and P. Patterson, An emergency aircraft evacuation simulation considering passenger emotions,, in Computers & Industrial Engineering, (2012), 746.  doi: 10.1016/j.cie.2011.11.012.  Google Scholar

[17]

B. Peterson, What we've learned so far from the Asiana Flight 214 investigation,, Popular Mechanics, ().   Google Scholar

[18]

, SeatGuru by TripAdvisor,, , (): 737.   Google Scholar

[19]

, SeatGuru by TripAdvisor,, , (): 777.   Google Scholar

[20]

S. Sharma, H. Singh and A. Prakash, Multi-agent modeling and simulation of human behavior in aircraft evacuations,, in Aerospace and Electronic Systems, (2008), 1477.  doi: 10.1109/TAES.2008.4667723.  Google Scholar

[21]

J. Tsai, et al., ESCAPES: Evacuation simulation with children, authorities, parents, emotions, and social comparison,, in The 10th International Conference on Autonomous Agents and Multiagent Systems, (2011), 457.   Google Scholar

[22]

Y. Zheng, J. Chen, J. Wei and X. Guo, Modeling of pedestrian evacuation based on the particle swarm optimization algorithm,, Physica A: Statistical Mechanics and its Applications, 391 (2012), 4225.  doi: 10.1016/j.physa.2012.03.033.  Google Scholar

show all references

References:
[1]

Y. Chuang, M. R. D'orsogna, D. C. Marthaler, A. L. Bertozzi and L. S. Chayes, State transitions and the continuum limit for a 2D interacting, self-propelled particle system,, Physica D, 232 (2007), 33.  doi: 10.1016/j.physd.2007.05.007.  Google Scholar

[2]

T. J. Cova and J. P. Johnson, A network flow model for lane-based evacuation routing,, Transportation Research Part A: Policy and Practice, 37 (2003), 579.  doi: 10.1016/S0965-8564(03)00007-7.  Google Scholar

[3]

F. Cucker and S. Smale, Emergent behavior in flocks,, IEEE Transactions on Automatic Control, 52 (2007), 852.  doi: 10.1109/TAC.2007.895842.  Google Scholar

[4]

K. Depart, et al., Aircraft evacuation testing: Research and technology issues,, Office of Technology Assessment, (): 1.   Google Scholar

[5]

R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory,, in Proceedings of the Sixth International Symposium on Micro Machine and Human Science, (1995), 39.  doi: 10.1109/MHS.1995.494215.  Google Scholar

[6]

E. Galea and J. P. Galparsoro, A computer-based simulation model for the prediction of evacuation from mass-transport vehicles,, Fire Safety Journal, 22 (1994), 341.  doi: 10.1016/0379-7112(94)90040-X.  Google Scholar

[7]

E. Galea and J. Galparsoro, Exodus: An Evacuation Model for Mass Transport Vehicles,, Papers, (1993).   Google Scholar

[8]

J. Garner, R. F. Chandler and E. Cook, GPSS Computer Simulation of Aircraft Passenger Emergency Evacuations,, U.S. Department of Transportation, (1978).   Google Scholar

[9]

R. Hassan, B. Cohanim, O. De Weck and G. Venter, A comparison of particle swarm optimization and the genetic algorithm,, in 46th AIAA/ASME/ASCE/AHS/ASC Structures, (2005), 1.  doi: 10.2514/6.2005-1897.  Google Scholar

[10]

D. Helbing, I. Farkas, P. Molnàr and T. Vicsek, Simulation of pedestrian crowds in normal and evacuation situations,, in Pedestrian and Evacuation Dynamics (eds. M. Schreckenberg and S. D. Sharma), (2002), 21.   Google Scholar

[11]

J. Izquierdo, I. Montalvo, R. Pérez and V. Fuertes, Forecasting pedestrian evacuation times by using swarm intelligence,, Physica A: Statistical Mechanics and its Applications, 388 (2009), 1213.  doi: 10.1016/j.physa.2008.12.008.  Google Scholar

[12]

P. Jorna, et al., Increasing the survival rate in aircraft accidents: Impact protection, fire survivability and evacuation,, European Transport Safety Council, (): 1.   Google Scholar

[13]

Y. Liu, W. Wang, H.-Z. Huang, Y. Li and Y. Yang, A new simulation model for assessing aircraft emergency evacuation considering passenger physical characteristics,, Reliability Engineering & System Safety, 121 (2014), 187.  doi: 10.1016/j.ress.2013.09.001.  Google Scholar

[14]

T. A. Lucas, Operator splitting for an immunology model using reaction-diffusion equations with stochastic source terms,, SIAM J. Numer. Anal., 46 (2008), 3113.  doi: 10.1137/070701595.  Google Scholar

[15]

T. A. Lucas, Maximum-norm estimates for an immunology model using reaction-diffusion equations with stochastic source terms,, SIAM J. Numer. Anal., 49 (2011), 2256.  doi: 10.1137/100794584.  Google Scholar

[16]

T. Miyoshi, H. Nakayasu, Y. Ueno and P. Patterson, An emergency aircraft evacuation simulation considering passenger emotions,, in Computers & Industrial Engineering, (2012), 746.  doi: 10.1016/j.cie.2011.11.012.  Google Scholar

[17]

B. Peterson, What we've learned so far from the Asiana Flight 214 investigation,, Popular Mechanics, ().   Google Scholar

[18]

, SeatGuru by TripAdvisor,, , (): 737.   Google Scholar

[19]

, SeatGuru by TripAdvisor,, , (): 777.   Google Scholar

[20]

S. Sharma, H. Singh and A. Prakash, Multi-agent modeling and simulation of human behavior in aircraft evacuations,, in Aerospace and Electronic Systems, (2008), 1477.  doi: 10.1109/TAES.2008.4667723.  Google Scholar

[21]

J. Tsai, et al., ESCAPES: Evacuation simulation with children, authorities, parents, emotions, and social comparison,, in The 10th International Conference on Autonomous Agents and Multiagent Systems, (2011), 457.   Google Scholar

[22]

Y. Zheng, J. Chen, J. Wei and X. Guo, Modeling of pedestrian evacuation based on the particle swarm optimization algorithm,, Physica A: Statistical Mechanics and its Applications, 391 (2012), 4225.  doi: 10.1016/j.physa.2012.03.033.  Google Scholar

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