# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 3 Development and Excel-Based Implementation of the Simulation Model
### Simulation of the Check-In Process  
Passenger check-in can currently be performed in three ways--conveniently from home via online websites and apps, using a self-service kiosk at the airport, or in person at a check-in desk staffed by airline or airport personnel. However, not every airport offers all these check-in options. Baggage check-in can either be carried out at the check-in desk or through self-service devices if available at the airport. To simplify the simulation model, it is assumed that each passenger must register for the flight at a check-in desk upon arrival at the airport. The model also includes the passenger's movement from the moment of arrival at the airport to the registration zone. Following the assumption that all passengers complete their check-in at staffed desks upon arrival, the model further presumes that they move directly from the airport entrance to the check-in area without delay. This reflects streamlined behavior, excluding  
Figure 3: Example of passenger arrival generation and distribution in the simulation model. potential stops at shops, restrooms, or other facilities, allowing for a clear simulation of passenger flow. Based on this premise, the transfer time from the point of arrival to the check-in zone is modeled as a random variable, influenced primarily by the spatial configuration of the terminal and average walking speed. Since passenger walking speeds naturally vary depending on individual characteristics such as age, fitness level, or travel habits, the model uses a normal probability distribution to simulate this movement. The mean of this distribution is determined by the physical layout of the terminal and the average walking pace, while the standard deviation reflects variability in individual movement speeds--some passengers move faster, while others slower. Other probability distributions can also be applied for movement time generation, considering situations where passengers may not proceed directly to the registration zone upon arrival. From a process standpoint, a staffed check-in desk can be regarded as a service channel. Therefore, for the purposes of the simulation model and description of the check-in process, queueing theory can be applied. Queueing theory enables the quantitative evaluation of various parameters in systems that handle mass service requests. A queueing system typically consists of three core elements--the service channel, the inflow of service requests, and the waiting queue [27]. In this context, the check-in desk represents the service channel, passengers represent the inflow of service requests (requiring registration for the flight), and a queue forms when the arrival rate exceeds the processing capacity of the desks. In practical terms, this means that passengers are arriving faster than they can be handled. In the simulation model, which is based on numerical data, the handling process is quantified using time-based parameters. The key measure in this context is the service time--the interval between the start of service at a check-in desk and the moment the desk becomes available for the next passenger. Service time can be modeled as either constant or random. In systems involving human operators, service time typically exhibits random behavior due to the variability in human performance and interaction. In the simplest queueing systems, service time is commonly modeled using an exponential probability distribution, which reflects the memoryless property typical for random service durations in human-operated systems [27]. This approach allows the model to realistically represent the randomness and unpredictability associated with human processing of passengers. It can be demonstrated that the throughput and other performance characteristics of a queueing system primarily depend on the mean service time (t_obs), rather than on the exact shape of the service time distribution function [27].