Design of aircraft cabin passenger layout scheme based on infectious disease model

Abstract

Abstract:

During the spread of the epidemic, regarding the risk of passengers of mutual infection in the aircraft cabin, it is

very necessary to optimize the layout scheme of passengers in the cabin and reduce the virus infection probability of

passenger. By studying the law of virus transmission in the aircraft cabin, this paper analyzed the influencing

factors of the probability of individuals being infected by the virus and the virus shedding rate in the cabin. The

match between passenger and seat was used as the decision variable, and the minimum sum of the cabin passengers

affected by the virus shedding rate of other passengers around was used as the objective function. Based on the

characteristics of passenger group flight, a allocation model for the passenger group cabin seat was established. This

paper designed an improved genetic algorithm, solved the model, and realized the algorithm using Matlab. Taking

A320 aircraft as an example, three passenger cases with different group layouts were tested. The results showed that

the proposed model and algorithm can provide an optimized seat allocation scheme within an acceptable time and

reduce the probability of infection among passengers.

Key words:

infectious disease model, passenger layout, seat allocation, genetic algorithm

CLC Number:

V223+ .2

GU Runping, ZHANG Yangzong, LIU Jiaming.

Design of aircraft cabin passenger layout scheme based on infectious disease model

[J]. Journal of Civil Aviation University of China, 2025, 43(3): 32-37.

References 13

[1]	姚瑞, 彭睿娟, 汪文煊．民航客舱服务中的新冠病毒防疫与卫生
	安全[J]．成都航空职业技术学院学报, 2020, 36(2): 71-74．
[2]	王光秋, 汲生成．病菌在机舱内传播分析综述[J]．航空学报, 2015,
	36(8): 2577-2587．
[3]	钱华, 郑晓红, 张学军．呼吸道传染病空气传播的感染概率的预
	测模型[J]．东南大学学报(自然科学版), 2012, 42(3): 468-472．
[4]	林家泉, 孙凤山, 李亚冲．客舱内呼吸道病原体传播机制与感染风
	险评估[J]．中国安全科学学报, 2020, 30(2): 146-151．
	[5]
	SCHMIDT M． A review of aircraft turnaround operations and simulations
[J]	． Progress in Aerospace Sciences, 2017, 92: 25-38．
	[6]
	SCHMIDT M, HEINEMANN P, HORNUNG M． Boarding and turnaround
	process assessment of single- and twin-aisle aircraft[C]//55th AIAA Aerospace Sciences Meeting, January 9-13, 2017, Grapevine, Texas． Reston,
	Virginia:AIAA, 2017: 1856．
	[7]
	AUDENAERT J, VERBEECK K, BERGHE G V． Multi-agent based simulation for boarding[C]//21st Belgian-Netherlands Conference on Artificial Intelligence (BNAIC 2009), Eindhoven, Netherlands, October 29-
	30, 2009: 123-130.
	[8]
	ZEINEDDINE H． A dynamically optimized aircraft boarding strategy[J]．
	Journal of Air Transport Management, 2017, 58: 144-151．
[9]	于玺华．微生物气溶胶的特性:兼论新型冠状病毒传播的防护[J]．暖
	通空调, 2022, 52(4): 108-112．
[10]	SMIESZEK T． A mechanistic model of infection:why duration and in－
	tensity of contacts should be included in models of disease spread [J]．
	Theoretical Biology & Medical Modelling, 2009, 6: 25．
[11]	MULLER S A, BALMER M, NEUMANN A, et al. Mobility traces and
	spreading of COVID-19[EB/OL]. (2020-03-20)[2023-03-01]. https://
	doi.org/10.14279/depositonce-9835.
[12]	SCHULTZ M, EVLER J, ASADI E, et al． Future aircraft turnaround op－
	erations considering post-pandemic requirements[J]． Journal of Air Transport Management, 2020, 89: 101886．
[13]	SCHULTZ M, SOOLAKI M． Analytical approach to solve the problem of
	aircraft passenger boarding during the coronavirus pandemic[J]． Trans－
	portation Research Part C: Emerging Technologies, 2021, 124: 102931．