Modern airports operate under high demands and pressures, and strive to satisfy many diverse, interrelated, sometimes conflicting performance goals. Airport performance areas, such as security, safety, and efficiency are usually studied separately from each other. However, operational decisions made by airport managers often impact several areas simultaneously. Current knowledge on how different performance areas are related to each other is limited. This paper contributes to filling this gap by identifying and quantifying relations and trade-offs between the detection performance of illegal items and the average queuing time at airport security checkpoints. These relations and trade-offs were analyzed by simulations with a cognitive agent model of airport security checkpoint operations. By simulation analysis a security checkpoint performance curve with three different regions was identified. Furthermore, the importance of focus on accuracy for a security operator is shown. The results of the simulation studies were related to empirical research at an existing regional airport.

In this work, AATOM, a microscopic agent-based model that simulates movement and operations in the airport terminal is presented. Specifcally, the model includes the main handling processes required for outbound passengers namely, check-in, security and border control. Furthermore, basic facilities for discretionary activities are modelled namely, bathrooms, restaurants and shops. The model has an accompanying architecture, the AATOM architecture that is described in this work as well. The objective of the model is to serve as a basis for several studies in the area of airport terminal operations. It will be used to investigate properties in the fields of security, efficiency, resilience and
possibly other fields like safety, and it will be used to investigate the relationship between any of these
areas.