# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 2 Conceptual Framework and Design Principles of the Simulation Model
This supports both educational purposes and practical use in airport operations or planning studies. Each passenger in the simulation is represented as a unique record with associated time variables and system characteristics, which evolve as the passenger progresses through the modelled process. The overall computational structure of the simulation is built on the following three fundamental methodological principles:  
* Queuing theory, which provides a formal framework for analyzing systems where entities wait for service. In our model, it defines the relationship between passenger arrival rates, available service capacity (e.g., check-in desks and security scanners), and queue formation. This theory supports the evaluation of key metrics such as average waiting times, queue lengths, and system utilization. * Monte Carlo simulation, which enables experimentation with stochastic inputs by generating random values based on statistical distributions. In the context of our Excel-based model, it allows for repeated simulation of varied passenger behaviors such as unpredictable arrival times, walking speeds, and service durations to observe how these variations affect system performance and bottleneck formation. * Discrete event logic, which models system behavior as a sequence of time-ordered events, each causing a change in system state. Although Excel is not inherently event-driven, our model simulates event-based transitions using time variables and conditional formulas. This structure allows us to track when a passenger enters or leaves a queue, begins or finishes service, and dynamically updates resource availability and queue progression based on those time events. Each phase of the modeled handling process is implemented as a separate computational block. These blocks are interconnected and influence each other, similar to real systems. Passenger records are passed from one block to the next, and relevant parameters such as arrival times, waiting periods, or service durations are calculated using Excel formulas with conditional logic. This structure allows the model to dynamically reflect queue conditions, identify process bottlenecks, and generate performance metrics, including average wait times and system utilization rates. To ensure clarity and computational feasibility, the presented simulation model is based on a set of defined assumptions that simplify the complexity of real-world passenger handling systems. These assumptions enable targeted experimentation with system dynamics while preserving the essential features of queue-based operations. Assumptions:
* All passengers proceed directly to the check-in area upon arrival at the airport, without intermediate stops or diversions (e.g., cafes, restrooms, or waiting areas). * Passenger arrival times follow a normal distribution, reflecting aggregated behavioral patterns observed in empirical studies of airport arrivals. * Service times at check-in and security checkpoints follow an exponential distribution, consistent with queuing theory models for stochastic service processes. * All check-in desks and security scanners are uniformly accessible and functionally identical (i.e., no priority lanes, staffing differences, or equipment malfunctions are considered). * Each passenger is treated as an independent entity, with no group behavior or correlation in arrival or service times. * Passengers are served in first-in, first-out (FIFO) order, with no overtaking, priority assignment, or service disruptions. Limitations:
* The model does not account for behavioral variability such as late arrivals, early check-ins, or skipped procedures (e.g., online check-in or self-service baggage drop). * Staff behavior, including fatigue, shift changes, or break scheduling, is not modeled. * The simulation assumes a static configuration and does not include adaptive mechanisms such as real-time resource reallocation or dynamic queue management. * Environmental factors such as terminal layout constraints, signage, or inter-terminal movement are not reflected.