Designing agent-based models is a difficult task. Some guidelines exist to aid modelers in designing their models, but they generally do not include specific details on how the behavior of agents can be defined. This paper therefore proposes the AbCDe methodology, which uses causal discovery algorithms to specify agent behavior. The methodology combines important expert insights with causal graphs generated by causal discovery algorithms based on real-world data. This causal graph represents the causal structure among agent-related variables, which is then translated to behavioral properties in the agent-based model. To demonstrate the AbCDe methodology, it is applied to a case study in the airport security domain. In this case study, we explore a new concept of operations, using a service lane, to improve the efficiency of the security checkpoint. Results show that the models generated with the AbCDe methodology have a closer resemblance with the validation data than a model defined by experts alone. @en

Airport security checkpoints are the most important bottleneck in airport operations, but few studies aim to empirically understand them better. In this work we address this lack of data-driven quantitative analysis and insights about the security checkpoint process. To this end, we followed a total of 2277 passengers through the security checkpoint process at Rotterdam The Hague Airport (RTM), and published detailed timing data about their journey through the process. This dataset is unique in scientific literature, and can aid future researchers in the modelling and analysis of the security checkpoint. Our analysis showed important differences between six identified passenger types. Business passengers were found to be the fastest group, while passengers with reduced mobility (PRM) and families were the slowest two groups. We also identified events that hindered the performance of the security checkpoint, in which groups of passengers had to wait long for security employees or other passengers. A total of 335 such events occurred, with an average of 2.3 passengers affected per event. It was found that a passenger that had a high luggage drop time was followed by an event in 27% of the cases, which was the most frequent cause. To mitigate this waiting time of subsequent passengers in the security checkpoint process, we performed an experiment with a so-called service lane. This lane was used to process passengers that are expected to be slow, while the remaining lanes processed the other passengers. It was found that the mean throughput of the service lane setups was higher than the average throughput of the standard lanes, making it a promising setup to investigate further.

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Despite enormous investments in airport security, terrorists have been able to find and exploit vulnerabilities at security checkpoints. Existing vulnerability assessment methodologies struggle with accounting for human behavior, and agent-based modelling forms a promising technique to overcome this limitation. This paper investigated how the decision-making and performance of human operators can be taken into account while assessing vulnerability at an airport security checkpoint. To this end, an agent-based model was designed, in which the performance of security operators was modelled using a functional state model, while decision making was modelled using decision field theory. Passengers and an attacker that brings a weapon to the security checkpoint were also explicitly modelled as agents. Simulation results indicate that the highest skilled operators outperformed their lowest skilled counterparts on analyzing X-ray images, but performed worse on both searching luggage and performing patdowns. Furthermore, results showed that a high focus on speed of security operators leads to a decrease in luggage searches and therefore increased vulnerability. More work is needed to calibrate and validate the simulation results, but initial results are promising. The agent-based model can be used by airport regulators and managers to understand the workings of their security checkpoint better and ultimately to reduce vulnerabilities.@en

Airports are important transportation hubs that reside in the heart of modern civilizations.They are of major economic and symbolic value for countries but are thereforealso attractive targets for adversaries. Over the years we have observed successful andunsuccessful terrorist attacks at airports, of which the recent Brussels Airport attack andIstanbul Atatürk Airport attack are two examples.A widely-used method to defend airports against these types of events is that of securityrisk management. Following this approach, security risks are quantified based onthreats, vulnerabilities, and consequences. These risks are then used as a basis to implementsecurity measures that can reduce the risks to acceptable levels. Several securityrisk management approaches were proposed before, such as attack trees and securitygames, but they struggle to include diverse human factors in their analysis. These factorsare inherently present in modern airports, as passengers, employees, and visitors areall humans. Furthermore, existing methods struggle to take other performance metrics,such as efficiency, into account.This thesis addresses these limitations by proposing a novel security risk managementapproach that relies on agent-based models and Monte Carlo simulations. Thisapproach builds on the existing security risk management framework but exploits theadvantages of the agent-based modelling paradigm. Agent-based models allow for theinclusion of rich cognitive, social and organizational models that enable the modellingof human behaviour. Furthermore, agent-based modelling is a suitable paradigm to estimatea variety of performance indicators, including airport efficiency.Two case studieswere performed to assess the performance of our agent-based securityrisk management approach. In these case studies we apply our approach to managesecurity risks at a regional airport, as well as an international airport.@en

Airports are attractive targets for terrorists, as they are designed to accommodate and process large amounts of people, resulting in a high concentration of potential victims. A popular method to mitigate the risk of terrorist attacks is through security patrols, but resources are often limited. Game theory is commonly used as a methodology to find optimal patrol routes for security agents such that security risks are minimized. However, game-theoretic models suffer from payoff uncertainty and often rely solely on expert assessment to estimate game payoffs. Experts cannot incorporate all aspects of a terrorist attack in their assessment. For instance, attacker behavior, which contributes to the game payoff rewards, is hard to estimate precisely. To address this shortcoming, we proposed a novel empirical game theory approach in which payoffs are estimated using agent-based modeling. Using this approach, we simulated different attacker and defender strategies in an agent-based model to estimate game-theoretic payoffs, while a security game was used to find optimal security patrols. We performed a case study at a regional airport, and show that the optimal security patrol is non-deterministic and gives special emphasis to high-impact areas, such as the security checkpoint. The found security patrol routes are an improvement over previously found security strategies of the same case study.

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Security risk management is essential for ensuring effective airport operations. This article introduces AbSRiM, a novel agent-based modeling and simulation approach to perform security risk management for airport operations that uses formal sociotechnical models that include temporal and spatial aspects. The approach contains four main steps: scope selection, agent-based model definition, risk assessment, and risk mitigation. The approach is based on traditional security risk management methodologies, but uses agent-based modeling and Monte Carlo simulation at its core. Agent-based modeling is used to model threat scenarios, and Monte Carlo simulations are then performed with this model to estimate security risks. The use of the AbSRiM approach is demonstrated with an illustrative case study. This case study includes a threat scenario in which an adversary attacks an airport terminal with an improvised explosive device. The approach provides a promising way to include important elements, such as human aspects and spatiotemporal aspects, in the assessment of risk. More research is still needed to better identify the strengths and weaknesses of the AbSRiM approach in different case studies, but results demonstrate the feasibility of the approach and its potential.@en

Analyzing agent-based models is a complex task. Agent-based models typically contain complex non-linear interactions between agents and generate emergent properties that cannot easily be explained. They are most commonly analyzed using sensitivity analysis techniques. While these techniques help understanding agent-based models better, they are not a one-size-fits-all solution. This paper explores the novel use of causal discovery algorithms from the field of causality as an additional means to analyze agent-based models. We propose the AbACaD methodology: Agent-based model Analysis using Causal Discovery. In this methodology, emergence in agent-based models is analyzed using causal discovery in combination with both machine learning and sensitivity analysis techniques. AbACaD combines different causal discovery algorithms, using a novel causal graph merging algorithm, to generate a causal graph based on agent-based simulation outcomes. This graph represents the causal relationships between the model parameters and the output variables of the model, and is then exploited to improve the understanding of emergent properties in the model. To demonstrate the effectiveness of AbACaD, it is applied to two models: the El Farol bar model, and an airport security and efficiency model. New emergent properties, such as the moment agents change their strategy in the El Farol bar model were identified. Furthermore, we found queue length to be an important factor in the number of casualties in an improvised explosive device (IED) attack. These emergent properties were well identified using AbACaD, but are hard to identify with traditional analysis techniques alone.

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AATOM, the Agent-based Airport Terminal Operations Model simulator is open-source, agent-based at its core, and contains several calibrated presets and templates of basic airport terminal components that can readily be used. Agents in this simulator follow the AATOM architecture, an activity-based architecture for human airport agents. This allows analysis based on agent activities, such as shopping and check-in, which is of vital interest for airports. The combination of agent-based modeling and the presence of basic airport terminal components makes AATOM a unique simulator, allowing the modeler to only focus on implementation of important features of their model. The usefulness of AATOM is demonstrated by presenting case studies in the areas of airport security, gate assignment and resilience.

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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.

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Both security and efficiency are important performance areas of air transport systems. Several methods have been proposed to assess security risks and estimate efficiency independently, but only few of these methods identify relationships between security risks and efficiency performance indicators. To analyze security, efficiency, and the relationships relations between them, an agent-based methodology was proposed in this work. This methodology combines an agent-based security risk assessment approach with agent-based efficiency estimation. The methodology was applied to a case study that analyzes security regarding an Improvised Explosive Device (IED) attack, different commonly used efficiency performance indicators in the aviation domain, such as queuing time for passengers, and the relationships between them. Results showed that reducing security risks and improving efficiency were not always conflicting objectives. Reducing the number of passengers before the security checkpoint was found to be an effective measure to reduce security risks and improve efficiency aspects. Furthermore, results showed that airports should attempt to spread passengers across the available space as much as possible to reduce the impact of an IED attack.@en

AATOM, the Agent-based Airport Terminal Operations Model simulator is open-source, agent-based at its core, and contains several calibrated presets and templates of basic airport terminal components that can readily be used. Agents in this simulator follow the AATOM architecture, an activity-based architecture for human airport agents. This allows analysis based on agent activities, such as shopping and check-in, which is of vital interest for airports. The combination of agent-based modeling and the presence of basic airport terminal components makes AATOM a unique simulator, allowing the modeler to only focus on implementation of important features of their model. The usefulness of AATOM is demonstrated by presenting case studies in the areas of airport security, gate assignment and resilience.@en

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.@en

We investigate the use of an Agent-based framework to identify and quantify the relationship between security and efficiency within airport terminals. In this framework, we define a novel Security Risk Assessment methodology that explicitly models attacker and defender behavior in a security scenario. It produces a security risk vector, quantifying the risks to the airport terminal. Efficiency is calculated in the same model using so-called key efficiency indicators. By using this framework, we aim to find and quantify factors that influence both security and efficiency in airport terminals. These factors can then be used to enable informed multi-objective decision making by airport management.@en

Security Risk Assessment is commonly performed by using traditional methods based on linear probabilistic tools and informal expert judgements. These methods lack the capability to take the inherent dynamic and intelligent nature of attackers into account. To partially address the limitations, researchers applied game theory to study security risks. However, these methods still rely on traditional methods to determine essential model parameters, such as payoff values. To overcome the limitations of traditional methods, we propose an approach which combines agent-based modelling with Monte Carlo simulations. Agent-based models allow more realistic representation of essential aspects and processes of socio-technical systems at cognitive, social and organisational levels. Such models can be used to estimate risks and parameters related to them. An application of the approach is illustrated by a case study of an airport security checkpoint.@en