A particle swarm optimization model of emergency airplane evacuations with emotion

Previous Article
Sparse control of alignment models in high dimension
NHM Home
This Issue
Next Article
Analyzing human-swarm interactions using control Lyapunov functions and optimal control

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

Abstract
Related Papers

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 and 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), 33-47. 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]	K. Depart, et al., Aircraft evacuation testing: Research and technology issues,, Office of Technology Assessment, (): 1.
[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]	P. Jorna, et al., Increasing the survival rate in aircraft accidents: Impact protection, fire survivability and evacuation,, European Transport Safety Council, (): 1.
[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]	B. Peterson, What we've learned so far from the Asiana Flight 214 investigation,, Popular Mechanics, ().
[18]	, SeatGuru by TripAdvisor,, , (): 737.
[19]	, SeatGuru by TripAdvisor,, , (): 777.
[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.

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), 33-47. 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]	K. Depart, et al., Aircraft evacuation testing: Research and technology issues,, Office of Technology Assessment, (): 1.
[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]	P. Jorna, et al., Increasing the survival rate in aircraft accidents: Impact protection, fire survivability and evacuation,, European Transport Safety Council, (): 1.
[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]	B. Peterson, What we've learned so far from the Asiana Flight 214 investigation,, Popular Mechanics, ().
[18]	, SeatGuru by TripAdvisor,, , (): 737.
[19]	, SeatGuru by TripAdvisor,, , (): 777.
[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.

[1]	Miao Yu. A solution of TSP based on the ant colony algorithm improved by particle swarm optimization. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 979-987. doi: 10.3934/dcdss.2019066
[2]	Qifeng Cheng, Xue Han, Tingting Zhao, V S Sarma Yadavalli. Improved particle swarm optimization and neighborhood field optimization by introducing the re-sampling step of particle filter. Journal of Industrial and Management Optimization, 2019, 15 (1) : 177-198. doi: 10.3934/jimo.2018038
[3]	Min Zhang, Gang Li. Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1413-1426. doi: 10.3934/dcdss.2019097
[4]	Ning Lu, Ying Liu. Application of support vector machine model in wind power prediction based on particle swarm optimization. Discrete and Continuous Dynamical Systems - S, 2015, 8 (6) : 1267-1276. doi: 10.3934/dcdss.2015.8.1267
[5]	Mohamed A. Tawhid, Kevin B. Dsouza. Hybrid binary dragonfly enhanced particle swarm optimization algorithm for solving feature selection problems. Mathematical Foundations of Computing, 2018, 1 (2) : 181-200. doi: 10.3934/mfc.2018009
[6]	Xia Zhao, Jianping Dou. Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization. Journal of Industrial and Management Optimization, 2019, 15 (3) : 1263-1288. doi: 10.3934/jimo.2018095
[7]	Abdulrazzaq T. Abed, Azzam S. Y. Aladool. Applying particle swarm optimization based on Padé approximant to solve ordinary differential equation. Numerical Algebra, Control and Optimization, 2022, 12 (2) : 321-337. doi: 10.3934/naco.2021008
[8]	Omar Saber Qasim, Ahmed Entesar, Waleed Al-Hayani. Solving nonlinear differential equations using hybrid method between Lyapunov's artificial small parameter and continuous particle swarm optimization. Numerical Algebra, Control and Optimization, 2021, 11 (4) : 633-644. doi: 10.3934/naco.2021001
[9]	Tao Zhang, Yue-Jie Zhang, Qipeng P. Zheng, P. M. Pardalos. A hybrid particle swarm optimization and tabu search algorithm for order planning problems of steel factories based on the Make-To-Stock and Make-To-Order management architecture. Journal of Industrial and Management Optimization, 2011, 7 (1) : 31-51. doi: 10.3934/jimo.2011.7.31
[10]	Zhongqiang Wu, Zongkui Xie. A multi-objective lion swarm optimization based on multi-agent. Journal of Industrial and Management Optimization, 2022 doi: 10.3934/jimo.2022001
[11]	Junjie Peng, Ning Chen, Jiayang Dai, Weihua Gui. A goethite process modeling method by Asynchronous Fuzzy Cognitive Network based on an improved constrained chicken swarm optimization algorithm. Journal of Industrial and Management Optimization, 2021, 17 (3) : 1269-1287. doi: 10.3934/jimo.2020021
[12]	Jacques Demongeot, Mohamad Ghassani, Mustapha Rachdi, Idir Ouassou, Carla Taramasco. Archimedean copula and contagion modeling in epidemiology. Networks and Heterogeneous Media, 2013, 8 (1) : 149-170. doi: 10.3934/nhm.2013.8.149
[13]	Shou Chen, Chen Xiao. Financial risk contagion and optimal control. Journal of Industrial and Management Optimization, 2022 doi: 10.3934/jimo.2022070
[14]	Urmila Pyakurel, Tanka Nath Dhamala. Evacuation planning by earliest arrival contraflow. Journal of Industrial and Management Optimization, 2017, 13 (1) : 489-503. doi: 10.3934/jimo.2016028
[15]	Geofferey Jiyun Kim, Jerim Kim, Bara Kim. A stochastic model of contagion with different individual types. Journal of Industrial and Management Optimization, 2020, 16 (5) : 2175-2193. doi: 10.3934/jimo.2019049
[16]	Christopher M. Kribs-Zaleta, Christopher Mitchell. Modeling colony collapse disorder in honeybees as a contagion. Mathematical Biosciences & Engineering, 2014, 11 (6) : 1275-1294. doi: 10.3934/mbe.2014.11.1275
[17]	Simone Göttlich, Sebastian Kühn, Jan Peter Ohst, Stefan Ruzika, Markus Thiemann. Evacuation dynamics influenced by spreading hazardous material. Networks and Heterogeneous Media, 2011, 6 (3) : 443-464. doi: 10.3934/nhm.2011.6.443
[18]	Tanka Nath Dhamala. A survey on models and algorithms for discrete evacuation planning network problems. Journal of Industrial and Management Optimization, 2015, 11 (1) : 265-289. doi: 10.3934/jimo.2015.11.265
[19]	Hari Nandan Nath, Urmila Pyakurel, Tanka Nath Dhamala, Stephan Dempe. Dynamic network flow location models and algorithms for quickest evacuation planning. Journal of Industrial and Management Optimization, 2021, 17 (5) : 2943-2970. doi: 10.3934/jimo.2020102
[20]	Tony Lyons. Particle paths in equatorial flows. Communications on Pure and Applied Analysis, 2022, 21 (7) : 2399-2414. doi: 10.3934/cpaa.2022041

2020 Impact Factor: 1.213

Tools

Metrics

Other articles
by authors

on AIMS
Junyuan Lin Timothy A. Lucas
on Google Scholar
Junyuan Lin Timothy A. Lucas

[Back to Top]