# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 1 Introduction  
The growing complexity and variability of airport operations, particularly in the context of passenger handling, are increasingly addressed through the lens of digital transformation. In recent years, aviation has not only embraced advanced automation and data-driven systems typical of Industry 4.0 but also begun to align with the emerging paradigm of Industry 5.0. This new vision, as defined by the European Commission [1], emphasizes a more human-centric, sustainable, and resilient approach to innovation, promoting the use of digital tools not only for efficiency but also for an increase in adaptability. In this context, simulation models and digital representations of airport processes, ranging from full-scale digital twins to simplified tools for flow and capacity analysis, play a key role in anticipating operational constraints and improving decision making. By enabling virtual experimentation, scenario testing, and the identification of bottlenecks, such tools reflect the fundamental aims of Industry 5.0 to support systems that are not only smart, but also responsive to change, scalable, and accessible even to smaller stakeholders with limited resources. This broader framework reinforces the relevance of simulation-driven models in airport capacity planning and positions them as integral components of the ongoing digital transformation of air transport. Within this framework, managing capacity at airports and improving handling processes remains a challenge that requires sophisticated analytical and simulation tools capable of dealing with uncertainty and variability. One key approach is the use of stochastic modelling to predict the behavior of systems in response to changes in demand and operating conditions. This methodology is proving particularly effective in mitigating congestion at airports and improving capacity management [2]. One of the fundamental theoretical methods for understanding the behavior of complex systems with high traffic loads, such as airport terminals, is the queueing theory. Using analytical and statistical approaches, it supports the optimization of decision-making processes and the design of efficient operational models, as reported in [3]. Moreover, its application is not limited to theory. The authors of the article [4] show how simulation models based on the queueing theory allow experimental verification of the proposed solutions, thus offering the possibility of optimization. One of the important methods that enable the practical application of queueing theory is discrete event simulation. This method supports the development of simulation models and provides a tool for the detailed analysis of dynamic processes in queueing systems. For example, by simulating passenger flows and baggage handling, bottlenecks can be identified, and various proposed solutions can be tested to remove them. The paper [5] applies these approaches to specific passenger and baggage handling processes, providing practical solutions and deepening the possibilities for effective planning and management in these areas. The use of discrete event simulation to analyze passenger flows is a beneficial tool for identifying and eliminating bottlenecks in different phases of airport processes. The paper [6] uses discrete event simulation to analyze passenger behavior in detail, from airport entry to boarding. The approach focuses on simulating the interactions between passengers and various systems within the terminal, thereby identifying key areas that may be causing congestion. A significant contribution of this paper is that, based on international standards, it provides concrete solutions to optimize handling processes. In this way, a more efficient passenger flow is ensured, contributing to smoother airport operations. The authors use the ARENA simulation environment to model a full sequence of departure-related processes, including terminal entry control, check-in, passport verification, and final security check.