# Evaluation of Aircraft Boarding Scenarios Considering Reduced Transmissions Risks

^{1}

^{2}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

#### 1.1. Review of Research on Virus Transmission in Aircraft

- Index Case: the first case of an infection chain.
- Secondary Attack Rate: the percentage of people infected out of the number of all contacts. A measure of how contagious a disease is, different from reproduction number R, which describes how many people an infected person infects on average.
- Pre-symptomatic: positively confirmed person, but before developing symptoms.
- Symptomatic: an infected person has apparent illness symptoms like fever, coughing and other
- Asymptomatic: positively confirmed the infected person who does not recognize any symptoms.

- -
- The airflow in an aircraft cabin is from above downwards (see Figure 1), reducing the probability that virus-laden air is ingested by other passengers,
- -
- The air in an aircraft cabin is exchanged rather frequently, about 20 times an hour.
- -
- The recirculated air is run through HEPA (High Efficiency Particulate Air) filters.
- -
- The air is quite dry at cruising altitude, which is problematic for Corona-type viruses.

#### 1.2. Passenger Boarding

#### 1.3. Previous Fields of Applications

#### 1.4. Focus and Structure of the Document

## 2. Derivation of Transmission Model

#### 2.1. Understanding of SARS-CoV2

#### 2.2. Modelling Approach

- -
- ${P}_{n,t}:\phantom{\rule{1.em}{0ex}}$ the probability of the person n to receive an infectious dose. This shall not be understood as “infection probability”, because this strongly depends on the immune response by the affected person.
- -
- $\theta :\phantom{\rule{1.em}{0ex}}$ the calibration factor for the specific disease
- -
- ${\mathrm{SR}}_{m,t}:\phantom{\rule{1.em}{0ex}}$ the shedding rate, the amount of virus the person m spreads during the time step t
- -
- ${i}_{nm,t}:\phantom{\rule{1.em}{0ex}}$ the intensity of the contact between n and m, which corresponds to their distance
- -
- ${t}_{nm,t}:\phantom{\rule{1.em}{0ex}}$ the time the person n interacts with person m during the time step t

- -
- If the cabin ventilation is active (which is highly desirable and probably mandatory in times of pandemic air travel) the air is circulated and quickly replaced. The exhale of a person does not remain in a place for very long. Hence the distance threshold is set lower than for other interior settings.
- -
- Droplets sink to the ground, and the cabin ventilation also injects fresh air into the upper part of the cabin and extracts at floor level. Hence a passenger located at a lower position is more susceptible to the virus exhaled from a passenger being located higher than vice versa. This is relevant when people in the aisle pass seated passengers.
- -
- The virus load increases with physical activity simply as more air is exchanged in the lungs. Talking (especially very loud, or even singing) also increases virus load in the exhale. Hence, the model considers moving passengers as having a higher shedding rate than seated passengers. Shedding rates are even higher when passengers store luggage in overhead bins or squeeze themselves into window seats.

#### 2.3. Calibration of Transmission Model

## 3. Passenger Boarding Model Using Operational and Individual Constraints

#### 3.1. Operational Constraints and Rules of Movement

#### 3.2. Model Adaption

- -
- A passenger is moving forward in the aisle, except the next position is blocked by another passenger. This blocking is counted as interaction for both passengers.
- -
- Entering the seat row demands a minimum of movements to reach the seat, which depends on the already used seats. All involved passengers are marked as interacting.
- -
- Each interaction is only counted one time (at the first appearance), to derive the number of individual contacts.

## 4. Scenario Analyses and Results

#### 4.1. Distance Keeping

#### 4.2. Reduction of Hand Luggage Items

#### 4.3. Transmission Approach

#### 4.4. Two Door Operations (Front and Rear Door)

## 5. Discussion and Outlook

## Author Contributions

## Funding

## Conflicts of Interest

## References

- ICAO. Effects of Novel Coronavirus (COVID-19) on Civil Aviation; Technical Report; International Civil Aviation Organisation: Montreal, QC, Canada, 2020. [Google Scholar]
- He, X.; Lau, E.H.; Wu, P.; Deng, X.; Wang, J.; Hao, X.; Lau, Y.C.; Wong, J.Y.; Guan, Y.; Tan, X.; et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat. Med.
**2020**. [Google Scholar] [CrossRef] [Green Version] - Gandhi, M.; Yokoe, D.S.; Havlir, D.V. Asymptomatic Transmission, the Achilles’ Heel of Current Strategies to Control Covid-19. New Engl. J. Med.
**2020**. [Google Scholar] [CrossRef] - IATA. Restarting Aviation Following COVID-19; IATA: Montreal, QC, Canada, 2020. [Google Scholar]
- Vandenberg, O. Development and potential usefulness of the COVID-19 Ag Respi-Strip diagnostic assay in a pandemic context. medRxiv
**2020**. [Google Scholar] [CrossRef] - Waltz, E. How Do Coronavirus Tests Work? IEEE Spectrum, 3 April 2020. [Google Scholar]
- Mangili, A.; Gendreau, M.A. Transmission of infectious diseases during commercial air travel. Lancet
**2005**, 365. [Google Scholar] [CrossRef] - Olsen, S.J.; Chang, H.L.; Cheung, T.Y.Y.; Tang, A.F.Y.; Fisk, T.L.; Ooi, S.P.L.; Kuo, H.W.; Jiang, D.D.S.; Chen, K.T.; Lando, J.; et al. Transmission of the severe acute respiratory syndrome on aircraft. N. Engl. J. Med.
**2003**, 349, 2416–2422. [Google Scholar] [CrossRef] - Hertzberg, V.S.; Weiss, H. On the 2-Row Rule for Infectious Disease Transmission on Aircraft. Ann. Glob. Health
**2016**, 82, 819–823. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Hertzberg, V.S.; Weiss, H.; Elon, L.; Si, W.; Norris, S.L. Behaviors, movements, and transmission of droplet-mediated respiratory diseases during transcontinental airline flights. Proc. Natl. Acad. Sci. USA
**2018**, 115, 3623–3627. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Schwartz, K.L.; Murti, M.; Finkelstein, M.; Leis, J.A.; Fitzgerald-Husek, A.; Bourns, L.; Meghani, H.; Saunders, A.; Allen, V.; Yaffe, B.; et al. Lack of COVID-19 transmission on an international flight. Can. Med Assoc. J.
**2020**, 192. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Qian, G.Q.; Yang, N.B.; Ding, F.; Ma, A.H.Y.; Wang, Z.Y.; Shen, Y.F.; Shi, C.W.; Lian, X.; Chu, J.G.; Chen, L.; et al. Epidemiologic and Clinical Characteristics of 91 Hospitalized Patients with COVID-19 in Zhejiang, China: A retrospective, multi-centre case series. QJM Int. J. Med.
**2020**, hcaa089. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Eldin, C.; Lagier, J.C.; Mailhe, M.; Gautret, P. Probable aircraft transmission of Covid-19 in-flight from the Central African Republic to France. Travel Med. Infect. Dis.
**2020**. [Google Scholar] [CrossRef] - Schmidt, M. A review of aircraft turnaround operations and simulations. Prog. Aerosp. Sci.
**2017**, 92, 25–38. [Google Scholar] [CrossRef] - Jaehn, F.; Neumann, S. Airplane boarding. Eur. J. Oper. Res.
**2015**, 244, 339–359. [Google Scholar] [CrossRef] - Schultz, M. Implementation and application of a stochastic aircraft boarding model. Transp. Res. Part C Emerg. Technol.
**2018**, 90, 334–349. [Google Scholar] [CrossRef] - Nyquist, D.C.; McFadden, K.L. A study of the airline boarding problem. J. Air Transp. Manag.
**2008**, 14, 197–204. [Google Scholar] [CrossRef] - Mirza, M. Economic Impact of Airplane Turn-Times. Aero Q.
**2008**, 4, 14–19. [Google Scholar] - Cook, A.; Tanner, G. European Airline Delay Cost Reference Values—Updated and Extended Values (Version 4.1); Technical Report; EUROCONTROL Performance Review Unit: Brussels, Belgium, 2015. [Google Scholar]
- Delcea, C.; Cotfas, L.A.; Paun, R. Agent-Based Evaluation of the Airplane Boarding Strategies’ Efficiency and Sustainability. Sustainability
**2018**, 10, 1879. [Google Scholar] [CrossRef] [Green Version] - Marelli, S.; Mattocks, G.; Merry, R. The Role of Computer Simulation in Reducing Airplane Turn Time. Boeing AERO Mag.
**2018**, 1. [Google Scholar] - Van Landeghem, H.; Beuselinck, A. Reducing passenger boarding time in airplanes: A simulation based approach. Eur. J. Oper. Res.
**2002**, 142, 294–308. [Google Scholar] [CrossRef] - Ferrari, P.; Nagel, K. Robustness of Efficient Passenger Boarding Strategies for Airplanes. Transp. Res. Rec.
**2005**, 1915, 44–54. [Google Scholar] [CrossRef] - van den Briel, M.H.L.; Villalobos, J.R.; Hogg, G.L.; Lindemann, T.; Mulé, A.V. America West Airlines Develops Efficient Boarding Strategies. INFORMS J. Appl. Anal.
**2005**, 35, 191–201. [Google Scholar] [CrossRef] - Bachmat, E.; Elkin, M. Bounds on the performance of back-to-front airplane boarding policies. Oper. Res. Lett.
**2008**, 36, 597–601. [Google Scholar] [CrossRef] - Schultz, M.; Schulz, C.; Fricke, H. Efficiency of Aircraft Boarding Procedures. In Proceedings of the 3rd International Conference on Research in Airport Transportation, Fairfax, VA, USA, 1–4 June 2008; pp. 371–377. [Google Scholar]
- Bachmat, E.; Khachaturov, V.; Kuperman, R. Optimal back-to-front airplane boarding. Phys. Rev. E Stat. Nonlinear Soft Matter Phys.
**2013**, 87, 062805. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Steffen, J.H. Optimal boarding method for airline passengers. J. Air Transp. Manag.
**2008**, 14, 146–150. [Google Scholar] [CrossRef] [Green Version] - Bazargan, M. A linear programming approach for aircraft boarding strategy. Eur. J. Oper. Res.
**2007**, 183, 394–411. [Google Scholar] [CrossRef] - Steffen, J.H. A statistical mechanics model for free-for-all airplane passenger boarding. Am. J. Phys.
**2008**, 76, 1114–1119. [Google Scholar] [CrossRef] [Green Version] - Frette, V.; Hemmer, P.C. Time needed to board an airplane: A power law and the structure behind it. Phys. Review. E Stat. Nonlinear Soft Matter Phys.
**2012**, 85, 011130. [Google Scholar] [CrossRef] - Bernstein, N. Comment on “Time needed to board an airplane: A power law and the structure behind it”. Phys. Review. E Stat. Nonlinear Soft Matter Phys.
**2012**, 86, 023101. [Google Scholar] [CrossRef] - Jafer, S.; Mi, W. Comparative Study of Aircraft Boarding Strategies Using Cellular Discrete Event Simulation. Aerospace
**2017**, 4, 57. [Google Scholar] [CrossRef] [Green Version] - Tang, T.Q.; Wu, Y.H.; Huang, H.J.; Caccetta, L. An aircraft boarding model accounting for passengers’ individual properties. Transp. Res. Part C Emerg. Technol.
**2012**, 22, 1–16. [Google Scholar] [CrossRef] - Qiang, S.J.; Jia, B.; Xie, D.F.; Gao, Z.Y. Reducing airplane boarding time by accounting for passengers’ individual properties: A simulation based on cellular automaton. J. Air Transp. Manag.
**2014**, 40, 42–47. [Google Scholar] [CrossRef] - Milne, R.J.; Salari, M. Optimization of assigning passengers to seats on airplanes based on their carry-on luggage. J. Air Transp. Manag.
**2016**, 54, 104–110. [Google Scholar] [CrossRef] - Milne, R.J.; Kelly, A.R. A new method for boarding passengers onto an airplane. J. Air Transp. Manag.
**2014**, 34, 93–100. [Google Scholar] [CrossRef] [Green Version] - Zeineddine, H. A dynamically optimized aircraft boarding strategy. J. Air Transp. Manag.
**2017**, 58, 144–151. [Google Scholar] [CrossRef] - Steiner, A.; Philipp, M. Speeding up the airplane boarding process by using pre-boarding areas. In Proceedings of the 9th Swiss Transport Research Conference, Ascona, Switzerland, 9–11 September 2009; p. 30. [Google Scholar]
- Wallace, R. The Flying Carpet. Available online: http://the-flying-carpet.com/wp-content/uploads/2013/11/The-Flying-Carpet-Information-Brochure.pdf (accessed on 29 June 2020).
- Wittmann, J. Customer-oriented optimization of the airplane boarding process. J. Air Transp. Manag.
**2019**, 76, 31–39. [Google Scholar] [CrossRef] - Milne, R.J.; Delcea, C.; Cotfas, L.A.; Salari, M. New methods for two-door airplane boarding using apron buses. J. Air Transp. Manag.
**2019**, 80, 101705. [Google Scholar] [CrossRef] - Schultz, M. Fast Aircraft Turnaround Enabled by Reliable Passenger Boarding. Aerospace
**2018**, 5, 8. [Google Scholar] [CrossRef] [Green Version] - Yazdani, D.; Omidvar, M.N.; Deplano, I.; Lersteau, C.; Makki, A.; Wang, J.; Nguyen, T.T. Real-time seat allocation for minimizing boarding/alighting time and improving quality of service and safety for passengers. Transp. Res. Part C Emerg. Technol.
**2019**, 103, 158–173. [Google Scholar] [CrossRef] - Bachmat, E.; Berend, D.; Sapir, L.; Skiena, S.; Stolyarov, N. Analysis of Airplane Boarding Times. Oper. Res.
**2009**, 57, 499–513. [Google Scholar] [CrossRef] [Green Version] - Schultz, M.; Kunze, T.; Fricke, H. Boarding on the critical path of the turnaround. In Proceedings of the 10th USA/Europe Air Traffic Management Research and Development Seminar, Chicago, IL, USA, 10–13 June 2013. [Google Scholar]
- Chung, C. Simulation Design Approach for the Selection of Alternative Commercial Passenger Aircraft Seating Configurations. J. Aviat. Technol. Eng.
**2012**, 2. [Google Scholar] [CrossRef] [Green Version] - Fuchte, J.C. Enhancement of Aircraft Cabin Design Guidelines with Special Consideration of Aircraft Turnaround and Short Range Operations. Ph.D. Thesis, Technische Universität Hamburg-Harburg, Hamburg, Germany, 2014. [Google Scholar]
- Schmidt, M.; Nguyen, P.; Hornung, M. Novel Aircraft Ground Operation Concepts Based on Clustering of Interfaces; SAE Technical Paper 2015-01-2401; SAE International: Warrendale, PA, USA, 2015; ISSN 0148-7191, 2688-3627. [Google Scholar] [CrossRef]
- Schmidt, M.; Heinemann, P.; Hornung, M. Boarding and Turnaround Process Assessment of Single- and Twin-Aisle Aircraft. In Proceedings of the 55th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, American Institute of Aeronautics and Astronautics, Grapevine, TX, USA, 9–13 January 2017. [Google Scholar] [CrossRef]
- Schultz, M. Dynamic change of aircraft seat condition for fast boarding. Transp. Res. Part C Emerg. Technol.
**2017**, 85, 131–147. [Google Scholar] [CrossRef] - Steffen, J.H.; Hotchkiss, J. Experimental test of airplane boarding methods. J. Air Transp. Manag.
**2012**, 18, 64–67. [Google Scholar] [CrossRef] [Green Version] - Gwynne, S.M.V.; Senarath Yapa, U.; Codrington, L.; Thomas, J.R.; Jennings, S.; Thompson, A.J.L.; Grewal, A. Small-scale trials on passenger microbehaviours during aircraft boarding and deplaning procedures. J. Air Transp. Manag.
**2018**, 67, 115–133. [Google Scholar] [CrossRef] - Schultz, M. Field Trial Measurements to Validate a Stochastic Aircraft Boarding Model. Aerospace
**2018**, 5, 27. [Google Scholar] [CrossRef] [Green Version] - Kierzkowski, A.; Kisiel, T. The Human Factor in the Passenger Boarding Process at the Airport. Procedia Eng.
**2017**, 187, 348–355. [Google Scholar] [CrossRef] - Miura, A.; Nishinari, K. A passenger distribution analysis model for the perceived time of airplane boarding/deboarding, utilizing an ex-Gaussian distribution. J. Air Transp. Manag.
**2017**, 59, 44–49. [Google Scholar] [CrossRef] - Schultz, M.; Rößger, L.; Fricke, H.; Schlag, B. Group Dynamic Behavior and Psychometric Profiles as Substantial Driver for Pedestrian Dynamics. In Pedestrian and Evacuation Dynamics 2012; Springer International Publishing: Cham, Switzerland, 2013; pp. 1097–1111. [Google Scholar] [CrossRef] [Green Version]
- Schultz, M.; Schulz, C.; Fricke, H. Enhanced Information Flow and Guidance in Airport Terminals using best Passenger‘s Visual Perception. In Proceedings of the 6th EUROCONTROL Innovative Research Workshop, Bretigny-sur-Orge, France, 4–6 December 2007; pp. 149–156. [Google Scholar]
- Schultz, M. Entwicklung eines individuenbasierten Modells zur Abbildung des Bewegungsverhaltens von Passagieren im Flughafenterminal. Ph.D. Thesis, Technische Universität Dresden, Faculty of Transport and Traffic Sciences “Friedrich List”, Dresden, Germany, 2010. [Google Scholar]
- Schultz, M.; Fricke, H. Managing Passenger Handling at Airport Terminal. In Proceedings of the 9th USA/Europe Air Traffic Management Research and Development Seminar, Berlin, Germany, 14–17 June 2011. [Google Scholar]
- Speitel, L.C. Fractional effective dose model for post-crash aircraft survivability. Toxicology
**1996**, 115, 167–177. [Google Scholar] [CrossRef] - Schultz, M. Faster aircraft boarding enabled by infrastructural changes. In Proceedings of the 2017 Winter Simulation Conference (WSC), Las Vegas, NV, USA, 3–6 December 2017. [Google Scholar] [CrossRef]
- Böhmer, M.M.; Buchholz, U.; Corman, V.M.; Hoch, M.; Katz, K.; Marosevic, D.V.; Böhm, S.; Woudenberg, T.; Ackermann, N.; Konrad, R.; et al. Outbreak of COVID-19 in Germany Resulting from a Single Travel-Associated Primary Case. Lancet Infect. Dis.
**2020**. [Google Scholar] [CrossRef] - Lavezzo, E.; Franchin, E.; Ciavarella, C.; Cuomo-Dannenburg, G.; Barzon, L.; Del Vecchio, C.; Rossi, L.; Manganelli, R.; Loregian, A.; Navarin, N.; et al. Suppression of COVID-19 outbreak in the municipality of Vo’, Italy. medRxiv
**2020**. [Google Scholar] [CrossRef] [Green Version] - Van Doremalen, N.; Bushmaker, T.; Morris, D.H.; Holbrook, M.G.; Gamble, A.; Williamson, B.N.; Tamin, A.; Harcourt, J.L.; Thornburg, N.J.; Gerber, S.I.; et al. Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1. N. Engl. J. Med.
**2020**, 382, 1564–1567. [Google Scholar] [CrossRef] - Ferretti, L.; Wymant, C.; Kendall, M.; Zhao, L.; Nurtay, A.; Abeler-Dörner, L.; Parker, M.; Bonsall, D.; Fraser, C. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science
**2020**. [Google Scholar] [CrossRef] [Green Version] - Smieszek, T. A mechanistic model of infection: Why duration and intensity of contacts should be included in models of disease spread. Theor. Biol. Med. Model.
**2009**, 6. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Smieszek, T. Models of Epidemics: How Contact Characteristics Shape the Spread of Infectious Diseases. Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2010. [Google Scholar] [CrossRef]
- Müller, S.A.; Balmer, M.; Neumann, A.; Nagel, K. Mobility traces and spreading of COVID-19. medRxiv
**2020**. [Google Scholar] [CrossRef] [Green Version] - Schultz, M. Stochastic Transition Model for Pedestrian Dynamics. In Pedestrian and Evacuation Dynamics 2012; Springer International Publishing: Cham, Switzerland, 2013; pp. 971–985. [Google Scholar] [CrossRef]
- Schultz, M.; Fricke, H. Improving Aircraft Turnaround Reliability. In Proceedings of the 3rd International Conference on Research in Airport Transportation, Fairfax, VA, USA, 1–4 June 2008; pp. 335–343. [Google Scholar]
- Schultz, M. Aircraft Boarding—Data, Validation, Analysis. In Proceedings of the 12th USA/Europe Air Traffic Management Research and Development Seminar, Seattle, WA, USA, 27–30 June 2017. [Google Scholar]
- Schultz, M.; Fricke, H. Stochastic Transition Model for Discrete Agent Movements. In Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2010; pp. 506–512. [Google Scholar] [CrossRef] [Green Version]

**Figure 1.**(

**Left**) Air ventilation pattern in a single-aisle cabin, here an Airbus A320. Note that other types have similar flow patterns. (

**Right**) High Efficiency Particulate Air (HEPA) filters being removed in an Airbus A380 aircraft from Emirates.

**Figure 2.**Position of individuals in groups depends on environmental conditions: free flow (

**left**) and congestion (

**right**).

**Figure 3.**Navigation in complex environments (airport terminal) under regular operational conditions, such as path finding with limited information.

**Figure 4.**Modeling and simulation of egress behavior considering fractional effective dose, a measure of airborne contaminants absorbed. A fire starts at the lower-deck of the third coach and smoke spreads through the whole coach. Passengers escape to the adjacent coaches. Affected passengers are color-coded from green (less impacted) to red (toxic dose), blue indicates no impact.

**Figure 5.**Implementation of technologies for active control of aircraft cabin environment and development of corresponding boarding procedures.

**Figure 6.**(

**Left**) Shedding rate of infected person. The increased rate is due to the relative positioning and increased physical activity. (

**Right**) Virus load depicted as contour plot around person as function of distance.

**Figure 7.**Calibrated viral load using the Air China Flight 112 from 15 March 2003. The numbers show the infection probability 50 boarding-deboarding runs. Note that infections have occurred even in remote seats, albeit the highest probability is close to the index case in 14E. The transmission probability is color-coded with white (no-contact), orange (minor probability), red (highly probable), and black (index case).

**Figure 9.**Overview of different boarding strategies: darker seats are boarded first, followed by black, blue, and green (

**left**). Implementation of operational constraints: darker seats are boarded first (

**right**).

**Figure 10.**Characteristic of boarding time (

**left**) and relative standard deviation of boarding time (

**right**) with increasing physical distance between passengers.

**Table 1.**Baseline simulation to determine regular boarding time and number of individual contacts by using average values and relative standard deviation (RSD).

Boarding Strategy | Boarding Time (%) | Number of Contacts (%) | |||
---|---|---|---|---|---|

Average | RSD | Average | RSD | ||

reference | random | 100.0 | 7.3 | 3.5 | 36 |

by block | back-to-front (2 blocks) | 95.9 | 7.3 | 3.5 | 36 |

optimized block (6 blocks) | 95.3 | 7.3 | 3.3 | 35 | |

by seat | outside-in | 79.5 | 7.1 | 2.8 | 39 |

reverse pyramid | 75.2 | 7.0 | 2.7 | 40 | |

individual | 65.8 | 7.4 | 2.2 | 53 | |

deboarding | 54.5 | 6.5 | 5.3 | 35 |

**Table 2.**Impact of physical distance rules (1.6 m) on the number of individual contacts, boarding time, and compensation of boarding time by 50% less hand luggage item. The reference boarding time equals 100% (random strategy), which corresponds to airline-specific implementations and reaches values between 10 and 20 min [54].

Reference | Keeping 1.6 m Minimum Distance in Aisle | |||||
---|---|---|---|---|---|---|

Boarding Strategy | Number of Contacts | Number of Contacts | Average Boarding Time (%) | |||

Average | RSD (%) | Average | RSD (%) | 100% Carry-on | 50% Carry-on | |

random | 3.5 | 36 | 0.9 | 85 | 198 | 154 |

back-to-front (2 blocks) | 3.5 | 36 | 0.9 | 86 | 220 | 169 |

optimized block (6 blocks) | 3.3 | 35 | 0.9 | 85 | 279 | 210 |

outside-in | 2.8 | 39 | 0.2 | 227 | 161 | 116 |

reverse pyramid | 2.7 | 39 | 0.2 | 261 | 185 | 128 |

individual | 2.2 | 53 | 0.2 | 271 | 114 | 104 |

deboarding | 5.3 | 35 | 5.0 | 36 | 97 | 68 |

**Table 3.**Evaluation of possible transmissions assuming one SARS-CoV2 passenger in the cabin and one door operations (front door).

Possible Transmissions | ||||||||
---|---|---|---|---|---|---|---|---|

0 m Distance | 1.6 m Distance | |||||||

100% Carry-on | 50% Carry-on | 100% Carry-on | 50% Carry-on | |||||

Boarding Strategy | Average | RSD | Average | RSD | Average | RSD | Average | RSD |

Value | (%) | Value | (%) | Value | (%) | Value | (%) | |

random | 5.9 | 68 | 4.2 | 83 | 1.6 | 124 | 1.1 | 145 |

back-to-front (2 blocks) | 5.6 | 65 | 3.9 | 81 | 1.4 | 123 | 1.0 | 144 |

optimized block (6 blocks) | 6.5 | 67 | 4.8 | 77 | 2.3 | 116 | 1.5 | 134 |

outside-in | 3.5 | 62 | 1.7 | 97 | 0.4 | 226 | 0.2 | 329 |

reverse pyramid | 3.0 | 56 | 1.3 | 99 | 0.2 | 291 | 0.1 | 467 |

individual | 2.0 | 92 | 0.8 | 154 | 0.2 | 301 | 0.1 | 489 |

deboarding | 10.0 | 36 | 8.0 | 42 | 9.7 | 34 | 7.8 | 43 |

**Table 4.**Evaluation of possible transmissions (Transm.) and boarding time assuming one SARS-CoV2 passenger in the cabin and two door operations (front and rear door).

0 m Distance | 1.6 m Distance | |||||
---|---|---|---|---|---|---|

Carry-on | Carry-on | Carry-on | Carry-on | |||

100% | 50% | 100% | 50% | |||

Boarding Strategy | Average | Average | Average | Boarding | Average | Boarding |

(Two Doors) | Transm. | Transm. | Transm. | Time (%) | Transm. | Time (%) |

random | 4.3 | 2.5 | 1.4 | 133 | 1.0 | 103 |

back-to-front (2 blocks) | 3.9 | 2.4 | 1.2 | 153 | 0.8 | 116 |

optimized block (6 blocks) | 5.5 | 3.4 | 1.5 | 166 | 1.0 | 125 |

outside-in | 1.9 | 0.6 | 0.3 | 107 | 0.1 | 77 |

reverse pyramid | 1.7 | 0.5 | 0.2 | 119 | 0.1 | 82 |

individual | 1.0 | 0.3 | 0.2 | 103 | 0.1 | 74 |

deboarding | 7.9 | 6.2 | 7.6 | 52 | 6.0 | 36 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Schultz, M.; Fuchte, J.
Evaluation of Aircraft Boarding Scenarios Considering Reduced Transmissions Risks. *Sustainability* **2020**, *12*, 5329.
https://doi.org/10.3390/su12135329

**AMA Style**

Schultz M, Fuchte J.
Evaluation of Aircraft Boarding Scenarios Considering Reduced Transmissions Risks. *Sustainability*. 2020; 12(13):5329.
https://doi.org/10.3390/su12135329

**Chicago/Turabian Style**

Schultz, Michael, and Jörg Fuchte.
2020. "Evaluation of Aircraft Boarding Scenarios Considering Reduced Transmissions Risks" *Sustainability* 12, no. 13: 5329.
https://doi.org/10.3390/su12135329