# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 1 Introduction
Thus, combining both modeling approaches supports robust scenario testing and more informed operational decision making, especially in complex and fluctuating environments such as airport terminals [7]. Combined optimization methods that integrate simulation with decision-support tools have also gained prominence, as they allow the evaluation of process configurations not only from a technical efficiency standpoint but also from operational, spatial, and user-oriented perspectives. An illustrative application of such an integrated approach is presented in [10], where discrete event simulation with a multi-criteria decision analysis (MCDA) framework are combined to assess alternative configurations of check-in and security control areas. In ExtendSim, the implemented model incorporates a range of performance indicators, including passenger throughput, queue lengths, processing times, staff workload, and spatial utilization, and enables the prioritization of solutions using a weighted scoring system. In this case study, the optimal configuration (variant 24) enabled the processing of 99.4% of passengers within 10 min, maintained baseline staffing levels, and reduced spatial requirements for queues by 31%, even under extended processing conditions introduced by new EU regulations. This hybrid method is particularly well-suited for medium-sized airports, where constraints on space and personnel necessitate trade-offs between conflicting objectives. In contrast to more technically demanding optimization techniques, MCDA offers transparency and interpretability, allowing airport decision makers to align resource allocation strategies with broader service quality goals and operational feasibility. Combined optimization methods that include simulation and analysis of airport processes are explored as additional approaches to enhance the efficiency of handling processes in [11]. This research uses the Tecnomatix Plant Simulation tool (developed by Siemens PLM Software) to optimize the check-in and safety inspection processes, considering operational constraints and costs. The results of this paper suggest that systematic improvement of these processes can significantly increase the overall capacity of an airport while maintaining high standards for passengers. In addition to analyzing flows, several studies explore direct optimization of airport processes through integrated simulation approaches [12], which focus on the use of queuing theory to evaluate terminal capacity. This paper proposes how existing queuing systems can be optimized based on passenger behavior analysis. For example, reorganization of check-in areas or dynamic queue management can significantly increase efficiency and reduce congestion during peak hours. Analysis of passenger behavior is important for proper capacity planning of airport terminals. The authors of paper [13] use agent-based modeling to simulate passenger behavior in airport terminals. This approach allows exploring different scenarios, such as passenger reactions to process changes, congestion, or space reorganization. The simulation results show how processes can be optimized based on the analysis of behavioral patterns. A more detailed analysis of passengers in specific phases of the handling process is presented in [14], which examines passenger boarding. This paper describes how simulations and validations of different boarding strategies can significantly reduce the time required to seat passengers and minimize delays. These insights have direct implications for the efficiency of overall boarding processes and can be integrated into broader airport operations modeling. At the macro level, previous methodologies are used to plan the capacity of airport terminals, which are facing increasing demands from passengers. Modelling passenger flows, as described in [15], allows not only the assessment of current capacities but also the design of expansion policies and the optimization of infrastructure planning.