Home // SIMUL 2025, The Seventeenth International Conference on Advances in System Modeling and Simulation // View article
Authors:
Ashraf Tantawy
Fanny Camelia
Ramona Bernhardt
Mohd Shoaib
Yaseen Zaidi
Ian Marr
Keywords: Markov Decision Process; Agent-Based Modeling; Airport infrastructure; Airport passenger; State machine; Decision-making; SysML; Discrete-Event Simulation; Dynamic Programming; Reinforcement Learning.
Abstract:
The end-to-end air passenger journey, from travel planning to arrival at the destination airport, encompasses a series of interdependent processes in which passenger behavior and airport infrastructure continuously influence one another. Passenger decision-making, such as arrival timing, use of services, and queue preferences, plays a central role in shaping these dynamics. Conversely, the design and efficiency of airport infrastructure can constrain or facilitate behavioral patterns, creating a feedback loop that is often overlooked in conventional modeling approaches. This study addresses the critical need to better understand the bidirectional relationship between passenger behavior and airport infrastructure. A hybrid modeling framework is developed, where Discrete Event Simulation (DES) for airport infrastructure is used to develop a passenger Agent-Based Model (ABM) via Markov Decision Process (MDP) formulation and optimal policy search. The model is informed by empirical data on passenger profiles, infrastructure configurations, and behavioral preferences. Preliminary analytical results highlight how small variations in passenger behavior can impact decision-making and infrastructure operation. The proposed framework will facilitate the design of behaviorally-informed, data-driven planning strategies for more resilient airport systems.
Pages: 54 to 59
Copyright: Copyright (c) IARIA, 2025
Publication date: September 28, 2025
Published in: conference
ISSN: 2308-4537
ISBN: 978-1-68558-300-2
Location: Lisbon, Portugal
Dates: from September 28, 2025 to October 2, 2025