High-fidelity digital twin for ground operations

23 April 2024

High-fidelity digital twin applied agent-based model for supporting predictable airport ground operations
^{M Luo, H Fricke, B Desart, SR Zapata, M Schultz. Available at SSRN 4806351}

Delays deriving from airport ground operations at the aircraft stand possess the capacity to propagate and consequently escalate across the intricate air traffic network. This knock-on effect often exerts a significant detrimental influence on downstream flights and airports. Fostering efficient aircraft ground handling can consolidate network stabilization through robust in- and outbound operations and, in certain instances, reduce subsequent cascading effects. Critical to this approach is the precise forecasting of potential delays at specific airports, combined with the effective management of associated bottlenecks. Such foresight is essential for the strategic allocation of resources and the execution of relevant strategies. Within A-CDM, the concept of airport collaborative decision-making, a reliable and predictable departure time from the stand, is deemed a crucial operational benchmark for both originating and destination airports. In this study, we develop a digital twin, supported by agent-based modeling, for a designated segment of Amsterdam Schiphol Airport, Pier H, which enables us to establish a high-fidelity virtual airport environment and simulate the entire aircraft ground handling activities over a day. All involved stakeholders and decision units are represented as agents, each performing specific activities aligned with common objectives conducive to an optimized collaborative decision-making process. The compliance with Target Off-Block Time (TOBT) for aircraft departures from stands is evaluated using a set of performance indicators across various scenarios, each encompassing distinct availability of airport ground resources and strategies for delay mitigation. We introduce a practical method for defining aircraft priority, aimed at guiding the sequence of the aircraft ground services, and incorporate the intelligence of ground service equipment for automatic recognition and prioritized servicing of aircraft. These elements form the basis of the priority-based service rule. Results validated through the robustness analysis highlight that the applied delay mitigation strategies effectively reduce the average departure delay of aircraft sequences. Additionally, the study contrasts the performance outcomes between priority-based and "first in, first out" (FIFO) service rules. Ensuring the appropriate provision of ground resources is pivotal for maintaining smooth airport throughput and preventing delays, addressing the issue of airport demand-capacity balance. Consequently, an optimization model for the total cost of the constrained airport resources and delays arising from schedule deviations is presented. The findings and insights from this study offer avenues for enhancing the predictability of airport ground operations, covering a spectrum from strategic and tactical planning to everyday operational activities.