# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 2 Conceptual Framework and Design Principles of the Simulation Model  
The process of passenger handling at airports represents a complex and dynamic system involving numerous interactions among various factors. Identifying bottlenecks in this process is essential for improving overall efficiency, process continuity, and enhancing passenger satisfaction. The passenger handling process at an airport encompasses multiple stages, from arrival at the airport to boarding the aircraft. The attributes of these stages vary depending on the specific airport. These variations arise due to factors such as the airport's size, demand for its services, terminal facilities, degree of modernization, use of new technologies, and the design and management of the handling process itself. Large international airports, for example, require fundamentally different systems to handle a significantly larger number of passengers compared to smaller airports, considering aspects like space, technology, equipment usage, and procedural management. Therefore, each airport and its processes require an individualized approach. Findings from observation and analysis of these processes can be generalized based on shared characteristics and differences. Given a general structure of the passenger handling process, a wide range of modeling strategies and methods can be applied, depending on the purpose of the model, the properties of the original system, and the capabilities of the simulation environment. As outlined in the introduction, the aim of this study is to demonstrate how a simulation model of passenger handling can be developed using commonly available software tools, with the goal of evaluating process capacity, identifying bottlenecks, and monitoring dynamic behavior under various scenarios. For this purpose, Microsoft Excel is selected as the simulation platform, owing to its accessibility, flexibility, and widespread use in operational and educational environments. In addition, such a platform provides a suitable foundation for later extensions, including the integration of artificial intelligence or migration to more sophisticated simulation tools. The model presented in this study does not replicate any specific airport but rather illustrates the general logic of how handling processes can be modeled, analyzed, and optimized using a structured and transparent approach. In order to demonstrate the modeling logic in a clear and targeted way, the simulation focuses on the following three main stages of the passenger handling process:  
1. Arrival at the airport and transfer to the check-in area;
2. Processing at staffed check-in desks;
3. Security screening at designated checkpoints. These stages were selected, because they represent key points in the terminal flow where passenger accumulation tends to occur and where delays have the most significant impact on system efficiency. Moreover, all three processes exhibit stochastic behavior, making them well-suited for simulation-based analysis. At the same time, their structure is sufficiently standardized across airports to allow for generalization, while remaining sensitive to variability in input parameters, service capacities, or arrival dynamics. Given the choice of Microsoft Excel as the simulation platform, the model is implemented through a calculation-based structure using its built-in statistical functions. The core logic relies on random number generation and the use of statistical distributions to represent real-life uncertainties, such as differences in passenger arrival times, walking speeds, or service durations. Specifically in our model, the normal distribution is used to simulate passenger arrival patterns, while the exponential distribution is applied to model service times at check-in and security points--both commonly used approaches in queuing system analysis. Unlike object-oriented or animation-based simulation tools, the Excel environment enables a fully transparent approach, where all generated values, formulas, and intermediary results remain visible and editable.