Airport terminals are complex sociotechnical systems, in which humans interact with diverse technical systems. A natural way to represent them is through agent-based modeling. However, this method has two drawbacks: it entails a heavy computational burden and the emergent properties are often difficult to analyze. The purpose of our research is therefore to accurately abstract and explain the dynamics of airport terminal operations by means of computationally efficient and interpretable surrogate models, based on an existing detailed agent-based simulation model. We propose a methodology consisting of two stages. Stage I involves the development of faithful surrogates. A sample is collected according to an active learning strategy, upon which Gaussian process regression, higher-order polynomials, gradient boosting, and random forests are fitted. Stage II then applies state-of-the-art techniques from the emerging field of explainable artificial intelligence to interpret and understand these models. Both model-agnostic and model-specific methods are considered, and their results are synthesized in order to explain the emergent properties. We prove the efficacy of this approach by conducting two case studies on AATOM, an existing Agent-based Airport Terminal Operations Model. The first case study examines the total expenditure on discretionary activities, such as shopping and dining. A combination of poor staffing strategies and high occupancy rates on certain flights was found to disrupt the terminal journey of passengers on subsequent flights. As a result of these knock-on phenomena, less free time is left for discretionary activities, which has a negative effect on the total expenditure. The second case study examines the throughput of security checkpoints. While throughput increases with passenger numbers, a clear point was observed where the checkpoint reaches its maximum capacity. This leads to longer queues and therefore higher waiting times. It even goes so far as to put passengers at risk of missing their flight, especially with poor staffing strategies. Altogether, we clearly observed the preservation of emergent phenomena in surrogate models, and conclude that their combination with interpretable machine learning is an effective way to explain the dynamics of complex sociotechnical systems.

The worldwide COVID-19 pandemic has had a tremendous impact on the aviation industry, with a reduction in passenger demand never seen before. To minimize the spread of the virus and to gain trust from the public in the airport operations’ safety, airports implemented measures, e.g., physical distancing, entry/exit temperature screening and more. However, airports do not know what the impact of these measures will be on the operations’ performance and the passengers’ safety when passenger demand increases back. The goal of this research is twofold. Firstly, to analyze the impact of current (COVID-19) and future pandemic-related measures on airport terminal operations. Secondly, to identify plans that airport management agents can take to control passengers’ flow in a safe, efficient, secure and resilient way. To model and simulate airport operations, an agent-based model was developed. The proposed model covers the main airport’s handling processes and simulates local interactions, such as physical distancing between passengers. The obtained results show that COVID-19 measures can significantly affect the passenger throughput of the handling processes and the average time passengers are in contact with each other. For instance, a 20% increase in check-in time (due to additional COVID-19 related paperwork at the check-in desk) can decrease passenger throughput by 16% and increase the time that passengers are in contact by 23%.

This paper presents a demonstration of our PAAMS 2021 paper using data-driven analysis of airport terminal operations and An Agent-based Airport Terminal Operations Model Simulator (AATOM). The goal of this paper is to demonstrate and analyze the impact of the current COVID-19 and future pandemic-related measures on airport terminal operations and to identify plans that airport management agents can take into account to control the flow of passengers in a safe, efficient, secure and resilient way. To analyze the impact of the identified COVID-19 measures on the airport operations, the existing agent-based AATOM model was need to be modified in order to implement these measures. In this paper, we illustrate a demo of a developed simulator tool by investigating the effects of different degrees of physical distancing rules among agents on the performances of the airport. In the demo session the attendees will have the possibility to (i) work with the simulator tool on different relevant parameters regarding different sections and agents in the airport; (ii) view and analyze different performance indicator analyzers of the simulator.

Discretionary activities such as retail, food, and beverages generate a significant amount of non-aeronautical revenue within the aviation industry. However, they are rarely taken into account in computational airport terminal models. Since discretionary activities affect passenger flow and global airport terminal performance, discretionary activities need to be studied in detail. Additionally, discretionary activities are influenced by other airport terminal processes, such as check-in and security. Thus, discretionary activities need to be studied in relation to other airport terminal processes. The aim of this study is to analyze discretionary activities in a systemic way, taking into account interdependencies with other airport terminal processes and operational strategies used to manage these processes. An agent-based simulation model for airport terminal operations was developed, which covers the main handling processes and passenger decision-making with discretionary activities. The obtained simulation results show that operational strategies that reduce passenger queue time or increase passenger free time can significantly improve global airport terminal performance through efficiency, revenue, and cost.