Modeling the impact of communication and human uncertainties on runway capacity in terminal airspace

Runway capacity is a major constraint in airport and terminal area operations. Thus, improving runway capacity is critical to reducing delays while maintaining safety and efficiency. More so, because operations are expected to become more complex and more susceptible to disruptions with the integration of highly automated and autonomous aircraft into the existing airspace. To this end, we investigate the potential impact of communication and human performance uncertainties on runway operations. Specifically, we consider these impacts within the context of an arrival scenario with two converging flows: a straight-in approach stream and a downwind stream merging into it. Both arrival stream are modeled using a modified Possion distribution that incorporate the separation minima as well as the runway occupancy time. Various system level uncertainties are addressed in this process, including communication link- and human-related uncertainties. In this research, we first build a Monte Carlo-based discrete-time simulation, where aircraft arrivals are generated by modified Possion processes subject to minimum separation constraints, simulating various traffic operations. The merging logic incorporates standard bank angle continuous turn-to-final, pilot response delays, and dynamic gap availability in real time. Then, we investigate an automated final approach vectoring model (i.e., Auto-ATC), in which inverse optimal control is used to learn decision advisories from human expert records. By augmenting trajectories and incorporating the aforementioned uncertainties into the planning scenario, we create a setup analogous to the discrete event simulation. For both studies, runway capacity is measured by runway throughput, the fraction of downwind arrivals that merge immediately without holding, and the average delay (i.e., holding time/distance) experienced on the downwind leg. This research provides a method for runway capacity estimation in merging scenarios, and demonstrates that aeronautical communication link uncertainties significantly affect runway capacity in current voice-based operations, whereas the impact can be mitigated in autonomous operational settings. This work emphasizes implications and guidance for future vectoring procedures in congested terminal areas, highlighting the uncertainty impacts to runway capacities, providing better decision support to ensure efficiency and safety of near-terminal operation operations. The code used in this research can be found from this LINK.