# Evaluation of Aircraft Boarding Scenarios Considering Reduced Transmissions Risks

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## Abstract

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## 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

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**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 |

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**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