Modeling the Impact of Communication and Human Uncertainties on Runway Capacity in Terminal Airspace

TL;DR

The paper analyzes how aeronautical communication uncertainties and human factors affect runway capacity in a terminal-area merging scenario with a straight-in and a downwind stream. It deploys a high-fidelity Monte Carlo discrete-event simulation of arrival flows and merging, parameterized by $T_{sep}$, $T_{Base}$, and $ au_{msg}$, and compares voice-based versus automated vectoring (RPAS) using an inverse optimal planning framework (MaxEnt IRL). Key contributions include an open-source DES framework for realistic merging, quantification of throughput, immediate-turn fraction, and downwind holding times under uncertainty, and a reliability-contour depiction of capacity under two planning paradigms. The findings indicate substantial performance losses from communication delays and human delays in legacy operations, while automation offers resilient improvements, guiding future air-traffic-management design and automation investments.

Abstract

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 Poisson 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.

Figures (24)

• Figure 1: Illustration of the simulated merging scenario in this study. Two arrival streams are included. In the scenario, the Straight-In arrivals have aligned with the runway centerline, while the Downwind arrivals have to determine if the gap between two Straight-Ins are sufficient enough to make a complete 180$^\circ$ continuous turn-to-final to merge into the straight-in flow, as well as maintaining proper separation and adjusted with runway occupancy time. To further complicate the simulation, the Downwind arrivals also suffer from the communication loss and pilot response delay uncertainties while making the turning decision.
• Figure 2: The illustration of communication signal reliability pang2025reliability.
• Figure 3: The pilot response model and message transaction time model. We adopt Gamma distribution of pilot response model, and truncated normal distribution in pilot-ATC message transaction time model.
• Figure 4: Cumulative distribution of runway occupancy time for common large weight class meijers2019data. From the figure, we show that the $95\%$ interval of runway occupancy time ($T_\nu$) for the arrival aircraft are between 62 seconds (for B737) to 66 seconds (for A321).
• Figure 5: Flowchart showing the downwind merging logic. The process begins when a downwind aircraft is abeam the runway threshold, at which time it evaluates the gap between successive arrivals already in the straight-in traffic stream. For each evaluation, the system first checks if the communication signal is continuously available for the duration of message transmission time $\tau_{msg}$. If the signal is available, then the simulation computes a potential merging time by summing the system latency $\varepsilon$, the pilot response time $\eta$, and the time required to complete the standard constant bank angle turn (i.e., 60 seconds). The system perform two step verification process, (i) It first checks if the proposed merge time will properly maintain the minimum separation time requirement $T_{sep}$ with both the preceding and following aircraft in the straight-in stream, as well as considering the system latency and pilot response delay; (ii) The second verification is performed just before the actual merge to ensure the gap is still available, taking into account any changes in the straight-in flows that may have occurred during the turn. If both verification are passed, the downwind aircraft is inserted into the schedule at the merged position in the straight-in queue, and the corresponding holding time in the downwind leg is recorded. If the gap is unavailable anytime during the process, the downwind aircraft will re-evaluate starting from the point of commitment. The simulation continues continues until either a valid merging opportunity is found, or the maximum simulation time is reached.