Airport systems rely on tightly connected digital and physical components, so cyber disruptions can affect both service performance and passenger movement. Existing airport simulation studies often focus on either queue-based passenger processing or pedestrian movement but rarely combine both in a framework suited for cyber-resilience analysis. This paper presents a hybrid simulation framework that integrates discrete-event simulation (DES), JuPedSim-based microscopic pedestrian modeling, and structured large language model (LLM) decision support to examine how cyber disruptions propagate through passenger-facing airport operations. The DES layer models service processes such as check-in, information desks, and security screening, while the pedestrian layer models movement, congestion, route choice, and spatial occupancy. Under degraded display or guidance conditions, the LLM generates structured passenger-level post-security decisions, such as going directly to the gate, checking a display, asking staff, waiting, visiting optional activity areas, or first moving to a wrong intermediate area. The framework is evaluated through a 500-passenger terminal case study with one baseline case and four disruption cases. Results show that check-in and security degradation produce the largest throughput loss, queue growth, and completion-time increase, while guidance degradation mainly affects post-security behavior. Spatial heatmaps further show where bottlenecks emerge and how congestion shifts across the terminal. Additional Rotterdam checkpoint validation, Palma benchmark analysis, and LLM ablation results support the framework’s ability to reproduce plausible queue, timing, throughput, and behavior-sensitive disruption patterns. The study provides a practical methodology for exploratory airport cyber-resilience assessment under coupled service, movement, and degraded-guidance conditions.
Katale et al. (Mon,) studied this question.