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.