The development of liquid hydrogen storage systems is a key aspect to enable future clean air transportation. However, safety analysis research for such systems is still limited and is hindered by the limited experience with liquid hydrogen storage in aviation. This paper presents the outcomes of a preliminary safety assessment applied to this new type of storage system, accounting for the hazards of hydrogen. The methodology developed is based on hazard identification and frequency evaluation across all system features to identify the most critical safety concerns. Based on the safety assessment, a set of safety recommendations concerning different subsystems of the liquid hydrogen storage system is proposed, identifying hazard scopes and necessary mitigation actions across various system domains. The presented approach has been proven to be suitable for identifying essential liquid hydrogen hazards despite the novelty of the technology and for providing systematic design recommendations at a relatively early design stage.

Both EASA and SESAR JU define a vision and roadmap towards an autonomous air traffic management system. Furthermore, past and ongoing SESAR JU projects investigate how to increase the efficiency and predictability of current operations by means of automation. In this paper, we explore the operational implications that result from fully-automated airport surface movement operations modelled with high realism. A hierarchical multi-agent system model was developed to coordinate and control all movements on the airport surface. It comprises the Airport Operations Agent to handle the flight schedule and runway configuration, the Routing Agent to compute conflict-free trajectories, and the Guidance Agents to instruct and monitor the Aircraft Agents while these execute the planned routes. The model incorporates the decisive processes and elements of airport surface movement operations such as pushback, engine-start, inbound and outbound holding, compliance to CTOT-slots, and wake turbulence separation for takeoffs. To compute conflict-free trajectories for all taxiing agents, we tailored and extended state-of-the-art multi-agent motion planning algorithms: the two-level routing algorithm combines Priority-Based Search (PBS) with Safe Interval Path Planning (SIPP). We defined different sizes of aircraft, accounted for a minimal safety distance between them, and calibrated their speed limits in curves with historic ADS-B data. Using the real-world flight schedules of two of the busiest days at Amsterdam Airport Schiphol, including different runway configurations, we examine the performance of the autonomous taxiing system with respect to the historic operations. For the considered simulation conditions, we show that the MAS yields 30% lower taxi times that vary less and are more predictable and increases runway capacity.

Both EASA and SESAR JU have outlined roadmaps towards an autonomous air traffic management system. Their long-term vision aligns with our previous work in which we studied the operational consequences of fully automated airport surface movement operations (ASM Ops) using a multi-agent system model that conducts multi-agent motion planning. However, such an automated system needs to be placed in a sociotechnical context, in which human operators and system users would be able to interact with automation in a seamless way. In the context of the SESAR JU project”ASTAIR - Auto-Steer Taxi at Airport”, workshops and interviews were conducted with human operators to identify operational requirements and preferred interactions with automated systems. In this paper, we explore how the requirements can be modelled and implemented in the multi-agent system for automated planning of ASM Ops to enable interactions with human operators. To this end, we present a conceptual agent-based framework for human-automation teaming in ASM Ops, introduce interactive tools for ATCOs to engage with the system, and showcase these interactions through a series of use cases derived from the workshops as well as historic data. Illustrated through the interactions of a fictitious Air Traffic Controller at Amsterdam Airport Schiphol, we demonstrate their practical implementation and broader aspects of such interactions in next-generation ASM Ops.

The increasing demand and complexity of air traffic management (ATM) systems necessitate significant advancements in automation to ensure safety and efficiency. Artificial intelligence (AI) and machine learning (ML) are emerging as promising solutions to manage this growing complexity, offering enhanced decision-making and predictive capabilities. However, the effectiveness of ML models in ATM heavily relies on the availability of extensive, high-quality data. In many cases, such data is scarce or incomplete, which presents a major barrier for training robust models. Synthetic data generation (SDG) is a viable solution to address this, enabling the creation of realistic datasets that unlock the ML value proposition. The Terminal Maneuvering Area (TMA) is a crucial segment of airspace characterized by high traffic density and diverse trajectory types, necessitating granular data to model these scenarios accurately. The main research objective of this work was to investigate the applicability of TimeGAN in generating synthetic 4-dimensional aircraft landing trajectories capable of capturing traffic patterns in this airspace, helping to analyze airspace constraints and delay propagation. The resulting synthetic trajectories were evaluated in terms of data diversity, fidelity and usefulness. The main challenge identified during the research was the imbalance in data classes, which affected the models’ ability to accurately capture data patterns, particularly in less frequent scenarios. Generating synthetic data based on separate groupings showed promise in addressing these imbalances, although this approach was sensitive to the designation of groups. This work proves the capability of TimeGAN in generating diverse, realistic trajectories that are difficult to differentiate from real historical data.

Background: Increased maritime trade has led to a surge in drayage operations, causing congestion and environmental issues in port areas. Truck Appointment Systems (TASs) are commonly used to manage truck arrival rates, yet transparency and equity in slot allocation remain problematic, fostering distrust between Licensed Motor Carriers (LMCs) and Marine Terminal Operators (MTOs). Methods: This study proposes a polycentric approach to improve truck scheduling and ensure that those impacted by decisions are involved in the decision-making process. A single-round auction mechanism focused on optimizing the truck hauling process through a pricing policy that promotes sincere bidding is introduced. The proposed approach employs an optimization strategy to achieve equitable coordination in truck synchronization through means of adaptable capacity management. Results: Numerical experiments assessing scenarios of noncollaborative behavior against partial collaboration between MTOs and LMCs demonstrate the effectiveness of the proposed approach in enhancing user satisfaction and terminal conditions for a case study focused on a medium-sized terminal. Collaboration between trucking companies is shown to increase utility per monetary unit spent on slot acquisition. Conclusions: The polycentric strategy offers a solution to TAS limitations by ensuring stakeholder participation with respect to flexibility and transparency by ensuring that those impacted by decisions are involved in the decision-making process.

Inspired by the vision of fully autonomous airside operations at Schiphol airport, this study aims to contribute to the short-term goal of automated aircraft ground handling. In this research, we design and evaluate a multi-agent system for planning of automated ground handling. There are two main components in the system: task allocation optimization and multi-agent path planning. To allocate tasks to ground support equipment (GSE) vehicles, an auction mechanism inspired by temporal sequential single item (TeSSI) auction is proposed. Ground handling tasks scheduling for GSE vehicles is modeled as several single-vehicle pickup and delivery optimization problems (SPDP), and the values of the objective functions are used to generate bids for GSE vehicle agents in the auction. Prioritized safe interval path planning for large agents (LA-SIPP) is used to plan collision-free paths for GSE vehicle agents in the model to execute tasks. The aim is to increase the success rates of allocating tasks and finding collision free paths without causing flight delays, given the limited resources such as a small number of available GSE vehicles, time windows constraints and conflicting interests of different agents. Due to the results, even for the instances with frequent flights and the most limited resources, the success rates of allocation and path planning were higher than 81% and 98%, respectively. Furthermore, periodic task allocation and path planning of the ground handling tasks for flights in three aircraft stands during a planning time window of the day, as well as replanning in case of disruptions were performed in a short CPU time. There is a lack of research dealing with the complete process of ground handling, since existing studies concerning the automation of ground handling operations involve fleet assignment or task scheduling models without an integration of detailed path planning. Our main contribution is to present a framework that combines task allocation and path planning for automation of ground handling operations and provides solutions using a multi-agent perspective.

Accessibility is one of the key performance indicators in the evaluation of a multimodal transport system and, as a result, transport planning has become increasingly more oriented towards it. Demand Responsive Transport (DRT) services have been proposed as a measure for increasing accessibility of a Public Transit (PT) network by servicing users in inaccessible areas. Through multimodal planning and coordination, a DRT service can be integrated within the extended PT network and supply the network optimally. In the context of PT users headed toward airports, an integrated DRT service is proposed for those with extended first-mile connections. This service makes use of taxis to transport users to transit points of a dedicated train line supplying a major European airport. Ride-sharing is considered, while optimal order of service and transit points for modal change are determined. To capture the decentralized nature of matching taxis to users, a multi-agent-based algorithm based on Distributed Constraint optimization Problems (DCOPs) is developed. Real-time information about routes and fixed schedules of the PT network are extracted via a dedicated routing Application Programming Interface (API). Experiments validate the applicability of the proposed solution by reporting a decrease in users’ first-mile travel time that is approximately analogous to the modal share the service captures.

Both EASA and SESAR JU define a vision and roadmap towards an autonomous air traffic management system. Furthermore, past and ongoing SESAR JU projects investigate how to increase the efficiency and predictability of current operations by means of automation. In this paper, we explore the operational implications that may result from fully-automated airport surface movement operations. In our model, a hierarchical multi-agent system coordinates and controls all movements on the airport surface. It comprises the Airport Operations Agent to handle the flight schedule and runway configuration, the Routing Agent to compute conflict-free trajectories, and the Guidance Agents to instruct and monitor the Aircraft Agents while these execute the planned routes. To compute conflict-free trajectories for all agents, we tailored state-of-the-art multi-agent motion planning algorithms to the requirements of taxiing operations: the two-level routing algorithm combines Priority-Based Search (PBS) with Safe Interval Path Planning (SIPP). It accounts for the different taxiing processes such as pushback, engine-start, or wake turbulence separation for takeoffs by defining an activity sequence for each agent. Furthermore, we include the kinematics and different sizes of the aircraft as well as a minimal safety distance between them. Using the real-world flight schedules of two of the busiest days at Amsterdam Airport Schiphol, including different runway configurations, we examine the performance of the autonomous taxiing system with respect to the historic operations. For the considered simulation conditions, we show that the MAS yields 15% lower taxi times for both arriving and departing flights, discuss how reliable these results are, and point out directions for future work.

The aviation industry has set an ambitious goal of reducing its climate impacts. Accordingly, airlines must balance their plans according to this goal with financial considerations. We developed a multi-objective framework to facilitate climate-aware network design by incorporating the objective to minimise the flight average temperature response (ATR) when optimising the airline network. We also assessed the operational improvements (OIs) which are introduced to improve sustainability in airline operations. In particular, we considered intermediate stop-overs (ISOs) and lower flight altitudes as OIs in our case studies. We analysed the impact of considering the climate impact in the planning of operations of three different airline types: one main-hub-and-spoke (KLM), one smaller multi-hub airline (TAP), and one low-cost carrier (EasyJet). The results show that airlines could also lower their environmental impact by 10–36% when considering the ATR as an objective. However, this would require an 8–20% reduction in profits. Adopting lower-altitude flying with ISO could mitigate their climate impact by 27–49% while reducing profits by approximately 6%. Our study highlights the importance of considering the airline network as a whole and demonstrates the potential benefits of operational improvements from a network perspective.

The airport terminals are complex sociotechnical systems, which are difficult to understand and their behavior is hard to predict. Hence, an agent-based model, the Agent-based Airport Terminal Operation Model (AATOM), has been designed to represent and analyze diverse airport terminal processes, actors, their behavior and interactions. The main issue with such models is the large computational requirements for simulating detailed processes, making it computationally inefficient. Furthermore, the dynamics of such models are difficult to understand. Therefore, the goal of this research is to approximate the dynamics of AATOM by a surrogate model, while preserving the important system properties. A methodology is suggested for training and validating a surrogate model, based on the Random Forest algorithm. The trained surrogate model is capable of approximating the AATOM simulation and identifying relative importance of the model variables with respect to the model outputs. Firstly, the results obtained contain an evaluation of the surrogate model accuracy performance, indicating that the surrogate model can achieve an average accuracy of 93% in comparison to the original agent-based simulation model. Nonetheless, one indicator, the number of missed flights, has shown to be more difficult to predict, with an average accuracy of 83%. Secondly, the results show that the airport resource allocation has an important impact on the efficiency of the airport terminal, with the two most important variables being the number of desks at the check-in and the number of lanes at the checkpoint. Last, the developed surrogate model was compared with a second Artificial Neural Network-based surrogate model built for the same agent-based model.

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.

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.

Resilience is commonly understood as the capacity for a system to maintain a desirable state while undergoing adversity or to return to a desirable state as quickly as possible after being impacted. In this paper, we focus on resilience for complex sociotechnical systems (STS), specifically those where safety is an important aspect. Two main desiderata for safety-critical STS to be resilient are adaptive capacity and adaptation. Formal studies integrating human cognition and social aspects are needed to quantify the capacity to adapt and the effects of adaptation. We propose a conceptual framework to elaborate on the concept of resilience of safety-critical STS, based on adaptive capacity and adaptation and how this can be formalized. A set of mechanisms is identified that is necessary for STS to have the capacity to adapt. Mechanisms belonging to adaptive capacity include situation awareness, sensemaking, monitoring, decision-making, coordination, and learning. It is posited that the two mechanisms required to perform adaptation are anticipation and responding. This framework attempts to coherently integrate the key components of the multifaceted concept of STS Equationsadaptive resilience. This can then be used to pursue the formal representation of Equationsadaptive resilience, its modeling, and its operationalization in real-world safety-critical STS.

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.

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.

Motivated by the need to understand and further optimize AOC decision making processes under uncertainty, this paper implements and evaluates the effects of operational uncertainties using Agent-Based Modelling and Simulation. The specific application concerns a challenging scenario composed of two consecutive disruptions. To evaluate the effects of uncertainties, an agent-based model of AOC processes has been developed using a logic-based ontology. Subsequently, this agent-based model is used to analyze the sensitivities of different model parameters. The simulation results provide novel insights into the effects of operational uncertainties on AOC decision-making and consequently airline performance. For the aircraft breakdown scenario considered, it is shown that adding buffers into the schedule promote a degree of self-recovery. The sensitivity analysis also reveals that transit buffer time and crew duty slack time act as tipping points for the airline operating costs. This demonstrates that ABMS allows to analyze and bring into light various sensitivities, which can be used in the early design phase to increase airline resilience, and train airline controllers for different environment states. The paper concludes that ABMS is a valuable approach that can enable a paradigm shift from reactive recovery to proactive recovery.

This paper proposes and evaluates a new airline disruption management strategy using multi-agent system modelling, simulation, and verification. This new strategy is based on a multi-agent negotiation protocol and is compared with three airline strategies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. To evaluate the new strategy, a rule-based multi-agent system model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation strategy outperforms the established strategies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.

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.

Use of Unmanned Aircraft Systems (UAS) is growing rapidly around the world. Very different types of UAS are used for applications such as aerial photography, inspection, emergency and Urban Air Mobility (UAM), operating in low altitude and urban environment, as well as in high altitude airspace integrated with the conventional air transportation system. As a new airspace user, UAS brings novel safety challenges to the current aviation system. For current aviation the main safety issues concern first and second parties, i.e. lives and property of crew and passengers. In contrast, the main safety concern of UAS operations is third party risk (TPR), i.e. the risk posed to people and properties that have no responsibility for the UAS operation and neither benefit in some way from the UAS operation. In order to ensure the safe operation of UAS, there is a need for an evaluation of safety regulation developments for UAS operations against relevant TPR indicators. The aim of this paper is to identify relevant TPR indicators for UAS operations and to evaluate safety regulations against these TPR indicators. The main finding is that current UAS safety regulations do not consider the accumulation of TPR contributions from many UAS flights per annum over rural or urban populations.