# Evaluating Classical Airplane Boarding Methods for Passenger Health during Normal Times
## 4 Description of the Agent-Based Model
### Passengers' Movement
#### 4.2.2 Agents' Characteristics for Open Seating Method  
In the case of the open seating boarding method, modeling the manner in which the passengers choose their seats might be a difficult task as mentioned by [PERSON] et al. [40], since human selection behavior needs to be included in the decision-making process. Considering a study conducted by Skyscanner.com ([[https://www.skyscanner.net/press-releases/skyscanner-reveals-perfect-seat-6a](https://www.skyscanner.net/press-releases/skyscanner-reveals-perfect-seat-6a)]([https://www.skyscanner.net/press-releases/skyscanner-reveals-perfect-seat-6a](https://www.skyscanner.net/press-releases/skyscanner-reveals-perfect-seat-6a)), accessed on 9 August 2021) on 1000 regular air travel passengers, it has been observed that approximatively 60% of the passengers prefer the window seat, while 40% opt for an aisle seat and less than 1% opt for a middle seat. Using a questionnaire with a 10-point Likert scale on 387 passengers, [PERSON] et al. [38] extracted the passengers' preference to window, middle, and aisle seats, their preference to front, middle, and rear of the airplane, and their preference for the crowded areas in the airplane. Based on the importance given to each of the three categories, they observed that most of the passengers prefer seats placed in non-crowded areas in the middle-front part of the airplane. Including the human behavior in the models that consider the open seating assignment has been made in the scientific literature through different approaches. One approach features the use of a seat attraction map built upon some fixed rules (e.g., [PERSON] et al. [40] consider all the passengers homogenous and having the same preference for the seats), while another approach gives a certain energy to each seat and updating the energy of the remained unselected seats upon each seat selection (e.g., the [PERSON] approach [14] based on the principles of statistical mechanics). Recently, an approach based on creating a fitness function for each passenger based on the preference to different parts of the airplane--front/middle/rear, window/middle/aisle, crowded/non-crowded area--andon the importance given to each type of category has been used in the research literature (e.g., [PERSON] et al. [38] determine a fitness function for each passenger through which the seats in the airplane form a preference map, each passenger having his/her own preference map based on the values extracted from the questionnaire). In this paper, we have used the approach featuring the calculation of a fitness function as it creates a personalized preference map for each agent based on the data extracted from questionnaires, benefiting from the heterogenous characteristic of the agents in agent-based modeling, rather than using a set of rules on homogenous agents. The fitness function is the following [38]:  
\[Fitness=iRow*pRow+iColumn*pColumn+iAgglomeration*AgglomerationState\]  
where  
* \(pRow\) and \(pColumn\) are passenger preference indicators, having values between 1 and 10;
* \(iRow\), \(iColumn\), and \(iAgglomeration\) are passenger importance indicators, having values between 1 and 10;
* \(AgglomerationState\) is a variable taking 0 or 10 as possible values. \[pRow=\left\{ \begin{array}{rl}pRowFront,&\text{if the row is in the front of the aircraft}\\ pRowMiddle,&\text{if the row is in the middle of the aircraft}\\ pRowBack,&\text{if the row is in the back of the aircraft}\end{array} \right.\]  
\[pColumn=\left\{ \begin{array}{rl}pColumnWindow,&\text{if the seat is near the window}\\ pColumnMiddle,&\text{if the seat is in the middle column}\\ pColumnAsile,&\text{if the seat is near the aisle}\end{array} \right.\]  
\[AgglomerationState=\left\{ \begin{array}{rl}0,&\text{if one or both seats located in the half $-$ row are occupied}\\ 10,&\text{if the seats in the same half $-$ row are both empty}\end{array}