A particle swarm optimization model of emergency airplane evacuations with emotion

Junyuan  Lin; Timothy A.  Lucas; Junyuan  Lin; Timothy A.  Lucas

doi:10.3934/nhm.2015.10.631

Networks and Heterogeneous Media

2015, Volume 10, Issue 3: 631-646. doi: 10.3934/nhm.2015.10.631

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A particle swarm optimization model of emergency airplane evacuations with emotion

Junyuan Lin ^{1
,},
Timothy A. Lucas ^{1
,}

1.
Pepperdine University, 24255 Pacific Coast Highway, Malibu, CA 90263

Received: 01 November 2014 Revised: 01 April 2015
Primary: 68T42, 90C90; Secondary: 68T05, 93C55.

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.
- Particle swarm optimization,
- contagion,
- airplane evacuation
Citation: Junyuan Lin, Timothy A. Lucas. A particle swarm optimization model of emergency airplane evacuations with emotion[J]. Networks and Heterogeneous Media, 2015, 10(3): 631-646. doi: 10.3934/nhm.2015.10.631

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Abstract

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.

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), 33-47. doi: 10.1016/j.physd.2007.05.007
[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), 579-604. doi: 10.1016/S0965-8564(03)00007-7
[3]	F. Cucker and S. Smale, Emergent behavior in flocks, IEEE Transactions on Automatic Control, 52 (2007), 852-862. doi: 10.1109/TAC.2007.895842
[4]	Office of Technology Assessment, Congress of the United States, 1-51.
[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, 39-43. doi: 10.1109/MHS.1995.494215
[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), 341-366. doi: 10.1016/0379-7112(94)90040-X
[7]	E. Galea and J. Galparsoro, Exodus: An Evacuation Model for Mass Transport Vehicles, Papers, Civil Aviation Authority, 1993.
[8]	J. Garner, R. F. Chandler and E. Cook, GPSS Computer Simulation of Aircraft Passenger Emergency Evacuations, U.S. Department of Transportation, Federal Aviation Administration, Office of Aviation Medicine, 1978.
[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, Structural Dynamics, and Materials Conference, 2005, 1-13. doi: 10.2514/6.2005-1897
[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), Springer, Berlin, 2002, 21-58.
[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), 1213-1220. doi: 10.1016/j.physa.2008.12.008
[12]	European Transport Safety Council, 1-48.
[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), 187-197. doi: 10.1016/j.ress.2013.09.001
[14]	T. A. Lucas, Operator splitting for an immunology model using reaction-diffusion equations with stochastic source terms, SIAM J. Numer. Anal., 46 (2008), 3113-3135. doi: 10.1137/070701595
[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), 2256-2276. doi: 10.1137/100794584
[16]	T. Miyoshi, H. Nakayasu, Y. Ueno and P. Patterson, An emergency aircraft evacuation simulation considering passenger emotions, in Computers & Industrial Engineering, Soft Computing for Management Systems, Vol. 62, 2012, 746-754. doi: 10.1016/j.cie.2011.11.012
[17]	Popular Mechanics, http://www.popularmechanics.com/technology/aviation/crashes/what-weve-learned-so-far-from-the-asiana-flight-214-investigation-16264162".
[18]	http://www.seatguru.com/airlines/Southwest_Airlines/Southwest_Airlines_Boeing_737-800_new.php.
[19]	http://www.seatguru.com/airlines/Asiana/Asiana_Boeing_777-200_ER_C.php.
[20]	S. Sharma, H. Singh and A. Prakash, Multi-agent modeling and simulation of human behavior in aircraft evacuations, in Aerospace and Electronic Systems, IEEE Transactions on, Vol. 44, 2008, 1477-1488. doi: 10.1109/TAES.2008.4667723
[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, AAMAS '11, International Foundation for Autonomous Agents and Multiagent Systems, 2, Richland, SC, 2011, 457-464.
[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), 4225-4233. doi: 10.1016/j.physa.2012.03.033

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