ORIGINAL RESEARCH article
Front. Aerosp. Eng.
Sec. Intelligent Aerospace Systems
Volume 4 - 2025 | doi: 10.3389/fpace.2025.1463425
This article is part of the Research TopicInsights in Intelligent Aerospace SystemsView all 3 articles
Formal Verification of a Machine Learning Tool for Runway Configuration Assistance
Provisionally accepted
- Ames Research Center, National Aeronautics and Space Administration, Moffet Field, United States
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This study explores the use of formal verification techniques to evaluate the efficacy of suggestions made by the Runway Configuration Assistance (RCA) tool, a machine learning-based decision support system that our group developed independently Memarzadeh et al. (2023). By using model-checking approaches, in particular Computation Tree Logic (CTL), this study verifies the compliance of the RCA tool with predefined safety regulations under different conditions of surface winds. By simulating a range of scenarios at three major US airports, Charlotte Douglas International Airport (CLT), Denver International Airport (DEN), and Dallas-Fort Worth International Airport (DFW), we thoroughly test the predictions of the tool to ensure that they meet strict safety margins with respect to crosswind and tailwind. The application of formal verification methods provides a strict analysis of the RCA tool, enhancing its validity and utility for possible implementation in an operational environment. Initially, a Monte Carlo simulation is carried out to analyze all possible wind conditions both velocity-wise and direction-wise. This part is intended to rigorously test the model against extreme, worst-case conditions to evaluate its performance. Second, we improve our methodology by performing simulations driven by realistic scenarios informed by actual historical data. This approach allows for a more accurate reflection of typical wind conditions (seen in the test airport) and provides a robust assessment of the model's effectiveness in maintaining safety standards under realistic environmental conditions.The model-checking reveals that overall 70% and 94% of the predictions satisfy the safety criteria in worst-case and realistic wind scenarios, respectively.
Keywords: Formal Verification, Model-checking, Air Traffic Management, machine learning, Safety criteria, Runway Configuration Management
Received: 11 Jul 2024; Accepted: 29 Jun 2025.
Copyright: © 2025 Razzaghi, Memarzadeh and Kalyanam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Pouria Razzaghi, Ames Research Center, National Aeronautics and Space Administration, Moffet Field, United States
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