# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 1 Introduction
One case study [16] further analyses the capacity of the check-in unit at Antalya Airport using the fuzzy logic method. This method allows a flexible response to dynamic changes in operational conditions and provides flexibility in managing the number of check-in desks. The optimization of the check-in process using fuzzy logic demonstrates how capacity constraints can be effectively managed without a significant negative impact on the flow of passenger check-in. While fuzzy logic allows for real-time adjustment of check-in resources based on predefined rules, other simulation-based approaches offer a complementary perspective by quantifying the passenger experience more explicitly. The authors of [17] introduce a metric called Level of Dissatisfaction Grade (LDG), calculated through Monte Carlo simulations, to assess how often passengers, segmented by type and purpose of travel, exceed acceptable waiting thresholds during check-in and security procedures. Their results reveal that business travelers exhibit the lowest tolerance for delays, with LDG values surpassing 0.3 (i.e., more than 30% of passengers experiencing dissatisfaction) when waiting times exceed 10 min, whereas leisure travelers showed similar dissatisfaction levels only after 15-20 min. This quantification enables a more nuanced differentiation of service level perceptions across user groups. While the authors of [9] approach Level of Service (LoS) from a cost-satisfaction trade-off perspective at the system level, the authors of [17] provide a micro-level behavioral lens to evaluate perceived service quality under various operational configurations. Simulations and evaluation of process improvement policies are further developed in [18], which focuses on analyzing the impact of new strategies in baggage screening processes. Using the AirSIM simulation tool, different approaches to redesign queuing systems are tested to optimize baggage screening time and improve the overall quality of service, thus achieving higher passenger satisfaction without negatively impacting security standards. An important advantage of simulation models is their ability to effectively simulate even non-standard, unplanned situations that often occur in real-life conditions. These models allow not only the analysis of standard operational scenarios but also the evaluation of crisis situations, such as system failures, which can significantly affect the smooth running of airport operations. The paper [19] deals with the optimized resource utilization in case of these system failures. Using simulation models, the impact of limited or incomplete resources on key processes such as check-in, security, and passport control is evaluated. These simulations show how processes can be flexibly adapted and optimized in times of emergency to minimize negative impacts on passengers and ensure business continuity. Building upon this crisis-oriented perspective, the study [20] proposes a multi-tier border inspection model that introduces differentiated control strategies based on probabilistic risk classification. The analytical framework divides passengers into distinct risk groups using estimated health risk values derived from pre-screening data, such as vaccination records or regional infection rates. Each group is routed through a dedicated inspection queue with specific processing speeds, detection accuracy, and resource capacities. Unlike uniform screening protocols, this approach optimizes both throughput and epidemiological safety by adapting inspection intensity to traveler risk profiles. Empirical validation through numerical simulations shows that a four-tier system achieves a safety level of 96.6% (the highest among tested configurations) while also maintaining acceptable rejection rates and system utilization. The model is particularly suitable for post-pandemic operations, where managing risk and maintaining passenger flow are equally critical.