Optimization of cabin seating arrangement strategies based on the Wells–Riley risk theory

Abstract

Civil aviation transport is an important source of global respiratory disease spread due to the closely-spaced environment. In order to reduce the probability of infection of passengers, an improved Wells-Riley model for cabin passenger risk assessment have been given in this work, the cabin ventilation and passenger nose and mouth orientation were considered. The model’s effectiveness has been verified with published data. Finally, how the load factor and use of an empty seat scheme are associated with the number of infected people was assessed. The results demonstrated that the number of infected people positively correlates with the passenger load factor, and the most suitable load factor can be determined by controlling the final number of infected people with the condition of the epidemic situation in the departure city. Additionally, infection risk was found to be lower among passengers in window seats than in those in aisle seats and middle seats, and keeping empty seats in the middle or aisle could reduce the cabin average probability of infection by up to 37.47%. Using the model developed here, airlines can determine the optimal load factor threshold and seating arrangement strategy to improve economic benefits and reduce the probability of passenger infection.

Uncertainty Analysis of Aircraft Center of Gravity Deviation and Passenger Seat Allocation Optimization

“…Moreover, not many studies have been conducted on cabin assignments, as their focus has been on cabin seat comfort and passenger seat selection preferences as well as other relevant factors. Liu, YX et al (2023) improved cabin seating distribution using the Wells-Riley model to reduce the probability of passengers contracting respiratory illnesses and to improve the economic efficiency of airlines[21]. From the perspective of an airline company, J Qiu et al (2017) collected and analyzed passengers' content and operating habits regarding cabin multimedia devices, built a model of passengers' personalities and preferences, predicted passengers' likely positive opinions of each other, and intelligently assigned them to adjacent seats[22].Castro, J et al (2021) proposed…”