M.F. von der Burg
Please Note
5 records found
1
AbstractCoordinating the movements of aircraft along the surface of busy airports is a complex task, involving both humans and machines. In response to the interrelated challenges of ever-increasing demand, emission reduction, and sustaining safety levels, technological advances are emerging which, in turn, necessitate novel concepts of operations (ConOps). To this end, diverse operational concepts with varying technological aspects, level of automation, and degree of control centralisation could potentially offer a solution. However, before such innovative ConOps are matured to be deployable at real-world airports, extensive evaluation based on computational modelling is necessary. To enable such detailed modelling and analysis of the large variety of concepts for next-generation airport surface movement operations, this paper proposes a generalised architecture based on the hierarchical-distributed multi-agent system modelling paradigm. We illustrate the generalised architecture by providing a specific model instance for a ConOps based on centralised planning and distributed, fully-automated control. As essential part of this model, we introduce the novel Multi-Agent Motion Planning on Airport Surfaces (AS-MAMP) algorithm to represent the decision-logic for coordinating all ground movements. The two-level solver builds on prominent prioritised planners such as Priority-Based Search (PBS) and its variant Greedy PBS. As current low-level solvers were insufficient to plan realistic 4D trajectories for ground movements, we developed the new Safe Interval Motion Planning (SIMP) algorithm. By defining activity sequences per agent, SIMP plans trajectories across the operational processes during taxiing such as pushback, tug coupling/decoupling, and engine-start. The motions of aircraft and ground vehicles are based on finite acceleration, and avoid dynamic obstacles in continuous space and time, necessitating to define states with feasible motion intervals. We benchmark AS-MAMP by varying its high-level prioritisation scheme, and exchanging SIMP with two existing low-level solvers, namely the Safe Interval Path Planning (SIPP) and A* algorithms. Based on scenarios on both a synthetic and a real-world airport layout, we demonstrate the efficacy of AS-MAMP to plan safe and efficient 4D trajectories. Moreover, we show that the model is able to handle runway throughput levels that match or exceed those of large European airports.
Towards Automated and Sustainable Airport Surface Movement Operations
Designing Models, Methods, and Tools for Next-generation Concepts of Operations
Against this backdrop, regulators, researchers, and practitioners increasingly view AI-enabled decision-support and automation as key enablers for future improvements. Yet, the literature lacks a modelling framework that simultaneously captures the interrelated operational processes, heterogeneous actors, and fidelity requirements necessary to evaluate next-generation concepts for airport surface movement operations (ASM Ops) in a realistic and systematic manner. Many existing models remain limited to isolated subsystems, single concepts, or strong simplifying assumptions, which restrict their ability to compare alternative future approaches at a detailed level. To address this gap, the objective of this thesis is to design models, methods, and tools to investigate next-generation concepts of operations (ConOps) for automated and sustainable ASM Ops.
The thesis is structured around eight research questions (RQs) and proceeds in three phases. The development phase (Chapters 2 to 5) first derives modelling requirements from the operational structure of ASM Ops and from the goal to represent surface movements at high fidelity while remaining computationally tractable (RQ 1). ASM Ops encompasses a hierarchy of tasks, ranging from strategic planning (long-term and high-level decisions) to scheduling (tactical allocation of resources), routing (tactical planning of movements), guidance (operational planning of movements), and finally movement execution (controlling aircraft and ground vehicles). This hierarchical decomposition, combined with the need for coordinated oversight to maintain the strict safety standards in aviation, motivates a hierarchical–distributed modelling approach.
To provide the required modularity, adaptability, and expressiveness in both fast-time and real-time simulations, this thesis adopts the multi-agent systems (MAS) paradigm. A generalised MAS architecture is proposed, comprising environmental objects and four agent categories aligned with the hierarchy of ASM Ops tasks (RQ 2). The architecture supports different allocations of responsibilities and different coordination mechanisms, enabling the instantiation of tailored model instances for specific ConOps while retaining a reusable simulation environment and component structure.
To support trajectory-based automation studies at airport scale (RQ 3), the thesis then outlines the Multi-Agent Motion Planning on Airport Surfaces (AS-MAMP) algorithm as the decision-logic for centralised path planning in a fully-automated operational setting, i.e. on EASA’s AI Level 3. AS-MAMP is a two-level solver that builds on Priority-Based Search (PBS) and its variants for high-level conflict resolution. Because existing low-level planners were insufficient to compute realistic 4D ground trajectories under operational constraints, the thesis introduces the novel Safe Interval Motion Planning (SIMP) algorithm. SIMP plans continuous-time trajectories in accordance with the operational processes in ASM Ops (e.g. pushback and engine-start, tug coupling/decoupling, and holding), motions based on finite acceleration, and conflict avoidance in continuous space and time.
The resulting planning approach is evaluated through structured benchmarking on a synthetic airport layout, where the high-level coordination component is compared against PBS variants and SIMP is benchmarked against SIPP and kinodynamic A*. The evaluation is complemented by experiments on the real-world layout of Amsterdam Airport Schiphol to assess scalability and operational relevance under realistic airport complexity. Taken together, these results establish the MAS architecture and AS-MAMP planning capability as an enabling foundation for subsequent operational analyses, while also identifying limitations and implementation challenges that arise when moving towards real-world deployment.
The validation phase (Chapters 6 to 8) applies the developed MAS framework in operational studies and subsequently strengthens its realism. Two far-term analyses investigate fully-automated ASM Ops under multi-engine taxiing (RQ 4) and under engine-off taxiing (RQ 5), examining system-level implications under dense traffic assumptions. Building on feedback from ATCOs and other operational experts gathered throughout the thesis, historical aircraft ground tracks are then analysed to improve the calibration of key movement parameters (RQ 6) so that the model is able to more accurately reproduce the historical operations. The MAS model is further extended to represent aircraft towing movements and to assess their impact on regular traffic (RQ 7). Based on these modifications, multiple operational model variations are compared to clarify the sensitivity of results to modelling assumptions and to discuss implications for next-generation surface operations.
Fully-automated operations pose significant implementation challenges in real-world settings, so human involvement remains required for the foreseeable future. Therefore, as a final exploration phase, Chapter 9 provides an outlook towards EASA’s AI Level 2 concepts by examining how human–automation interaction mechanisms can be embedded into the MAS model to enable operator involvement (RQ 8). These demonstrations focus on technical feasibility – illustrating how interfaces and information exchange could be embedded into the MAS model – rather than validated evidence from human-in-the-loop experiments. Chapter 10 concludes by synthesising the findings across all research questions, reflecting on limitations and implications for future ASM Ops, and outlining directions for continued development and socio-technical validation. ...
Against this backdrop, regulators, researchers, and practitioners increasingly view AI-enabled decision-support and automation as key enablers for future improvements. Yet, the literature lacks a modelling framework that simultaneously captures the interrelated operational processes, heterogeneous actors, and fidelity requirements necessary to evaluate next-generation concepts for airport surface movement operations (ASM Ops) in a realistic and systematic manner. Many existing models remain limited to isolated subsystems, single concepts, or strong simplifying assumptions, which restrict their ability to compare alternative future approaches at a detailed level. To address this gap, the objective of this thesis is to design models, methods, and tools to investigate next-generation concepts of operations (ConOps) for automated and sustainable ASM Ops.
The thesis is structured around eight research questions (RQs) and proceeds in three phases. The development phase (Chapters 2 to 5) first derives modelling requirements from the operational structure of ASM Ops and from the goal to represent surface movements at high fidelity while remaining computationally tractable (RQ 1). ASM Ops encompasses a hierarchy of tasks, ranging from strategic planning (long-term and high-level decisions) to scheduling (tactical allocation of resources), routing (tactical planning of movements), guidance (operational planning of movements), and finally movement execution (controlling aircraft and ground vehicles). This hierarchical decomposition, combined with the need for coordinated oversight to maintain the strict safety standards in aviation, motivates a hierarchical–distributed modelling approach.
To provide the required modularity, adaptability, and expressiveness in both fast-time and real-time simulations, this thesis adopts the multi-agent systems (MAS) paradigm. A generalised MAS architecture is proposed, comprising environmental objects and four agent categories aligned with the hierarchy of ASM Ops tasks (RQ 2). The architecture supports different allocations of responsibilities and different coordination mechanisms, enabling the instantiation of tailored model instances for specific ConOps while retaining a reusable simulation environment and component structure.
To support trajectory-based automation studies at airport scale (RQ 3), the thesis then outlines the Multi-Agent Motion Planning on Airport Surfaces (AS-MAMP) algorithm as the decision-logic for centralised path planning in a fully-automated operational setting, i.e. on EASA’s AI Level 3. AS-MAMP is a two-level solver that builds on Priority-Based Search (PBS) and its variants for high-level conflict resolution. Because existing low-level planners were insufficient to compute realistic 4D ground trajectories under operational constraints, the thesis introduces the novel Safe Interval Motion Planning (SIMP) algorithm. SIMP plans continuous-time trajectories in accordance with the operational processes in ASM Ops (e.g. pushback and engine-start, tug coupling/decoupling, and holding), motions based on finite acceleration, and conflict avoidance in continuous space and time.
The resulting planning approach is evaluated through structured benchmarking on a synthetic airport layout, where the high-level coordination component is compared against PBS variants and SIMP is benchmarked against SIPP and kinodynamic A*. The evaluation is complemented by experiments on the real-world layout of Amsterdam Airport Schiphol to assess scalability and operational relevance under realistic airport complexity. Taken together, these results establish the MAS architecture and AS-MAMP planning capability as an enabling foundation for subsequent operational analyses, while also identifying limitations and implementation challenges that arise when moving towards real-world deployment.
The validation phase (Chapters 6 to 8) applies the developed MAS framework in operational studies and subsequently strengthens its realism. Two far-term analyses investigate fully-automated ASM Ops under multi-engine taxiing (RQ 4) and under engine-off taxiing (RQ 5), examining system-level implications under dense traffic assumptions. Building on feedback from ATCOs and other operational experts gathered throughout the thesis, historical aircraft ground tracks are then analysed to improve the calibration of key movement parameters (RQ 6) so that the model is able to more accurately reproduce the historical operations. The MAS model is further extended to represent aircraft towing movements and to assess their impact on regular traffic (RQ 7). Based on these modifications, multiple operational model variations are compared to clarify the sensitivity of results to modelling assumptions and to discuss implications for next-generation surface operations.
Fully-automated operations pose significant implementation challenges in real-world settings, so human involvement remains required for the foreseeable future. Therefore, as a final exploration phase, Chapter 9 provides an outlook towards EASA’s AI Level 2 concepts by examining how human–automation interaction mechanisms can be embedded into the MAS model to enable operator involvement (RQ 8). These demonstrations focus on technical feasibility – illustrating how interfaces and information exchange could be embedded into the MAS model – rather than validated evidence from human-in-the-loop experiments. Chapter 10 concludes by synthesising the findings across all research questions, reflecting on limitations and implications for future ASM Ops, and outlining directions for continued development and socio-technical validation.
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.
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.