# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 1 Introduction
Their model is enriched by integration with a decision-support system that draws from operational databases, such as flight schedules, staffing plans, and technical resource allocations, and allows scenario testing of system reconfigurations. One of the key findings of the study is the identification of the check-in area as the most critical bottleneck, where average waiting time reached 68.88 min when only one X-ray screening unit was available. By operating two units in parallel, the waiting time was reduced to 15.57 min. In contrast, stages such as passport control and final security screening maintained excellent performance with waiting times consistently below one minute, in accordance with IATA Level of Service standards. These results confirm that bottlenecks may shift between subsystems depending on resource allocation, and that a holistic simulation approach is essential to prevent suboptimization. The authors also highlight the secondary economic benefits of improved flow efficiency, as shorter queues increase passengers' exposure to retail areas, enhancing commercial revenue potential. In addition, a combination of static and dynamic capacity analyses is often used in simulation models to provide both high-level planning perspective and adaptive operational insight. Static models are typically based on average arrival rates, fixed staff allocations, and standardized process durations, offering baseline estimates of system capacity under scheduled or ideal conditions [7]. These models often rely on simplified assumptions from queuing theory, like in [2], such as Poisson arrival distributions and exponential service times. For instance, one study [8] presents an M/M/k queuing model aimed at optimizing the number of active check-in desks while minimizing labor costs. Their method enables the calculation of optimal server utilization based on input parameters such as passenger arrival rates and target service levels. Simulation results demonstrate that maintaining desk utilization around 85% minimizes average waiting time while avoiding unnecessary staffing surpluses. Although their model abstracts from behavioral or multistage interactions, it offers a transferable, analytically tractable tool especially suitable for quick planning scenarios in smaller airport environments. A more nuanced static approach is proposed by the authors of the study [9], who introduce a bi-objective optimization model for determining the optimal number of check-in desks by jointly minimizing operational costs and maximizing passenger satisfaction. Instead of focusing solely on system efficiency, their framework integrates economic and experiential factors, including the value of passenger time and acceptable service levels as defined by Level of Service (LoS) standards. The model assesses trade-offs between cost savings from reduced staffing and the negative impacts of longer waiting times on perceived service quality. Their findings indicate that a moderate increase in operational cost, such as maintaining a slightly higher number of active desks than what is strictly necessary for throughput, can result in a disproportionately larger gain in user satisfaction. This highlights the importance of incorporating user-centric metrics into resource allocation strategies, even within deterministic, analytically structured models. However, static models may fail to capture temporary saturation effects or peak congestion periods. In contrast, dynamic models allow for flexible adaptation to actual operating conditions by incorporating stochastic variation in arrival patterns, service times, and resource availability. This enables a more nuanced identification of transient bottlenecks and operational breakdowns. As demonstrated by the results in [6], even small changes in resource configuration, such as operating an additional screening unit, can drastically alter system performance.