Advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance
While favored by air traffic controllers (ATCOs), ATCO- conformal conflict resolution often lacks operational efficiency due to such potential issues as excessive aircraft deviations and higher separation buffers. This study investigated the potential of advancing beyond conformal conflict r...
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sg-ntu-dr.10356-1793792024-08-06T15:30:52Z Advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance Guleria, Yash Pham, Duc-Thinh Alam, Sameer 11th International Conference on Research in Air transportation (ICRAT 2024) Air Traffic Management Research Institute Computer and Information Science Engineering Air traffic conflict resolution Machine learning Supervised learning Air traffic controllers Conformal automation Human-AI teaming While favored by air traffic controllers (ATCOs), ATCO- conformal conflict resolution often lacks operational efficiency due to such potential issues as excessive aircraft deviations and higher separation buffers. This study investigated the potential of advancing beyond conformal conflict resolution advisories in en- route air traffic control by balancing optimality and conformance of the advisories to bridge the gap between ATCO conformance and maneuver efficiency. Firstly, a machine learning (ML) approach is proposed to generate various conflict resolution advisories, includ- ing balanced options. Then, initial human-in-the-loop experiments with 6 experienced ATCOs were conducted, wherein ATCOs were required to rank conflict resolution advisories based on their preferences, for the presented conflict scenarios. Given the trade-off between conformance and efficiency of the conflict resolution ma- neuvers, this research investigated the ATCOs’ preferences among the resolution options provided, for the conflict scenarios. Results indicated a strong preference for conformal conflict resolution in 70 of 114 scenarios collected, with a significant inclination towards balanced resolutions in 62 scenarios, suggesting a move towards more efficient strategies without compromising conformance. Cu- mulatively, conformal and balanced conflict resolutions were the most favorable and second most favorable choices for 77.19% and 75.4% of the scenarios, respectively. ATCO feedback also indicated that such a conflict resolution advisory mechanism could prove useful in improving controller decision-making. These findings highlighted the need for further research into adaptive conflict resolution models that can potentially better balance the tradeoffs between optimal- and conformal resolution models. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Submitted/Accepted version This work is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme 2024-07-31T02:14:01Z 2024-07-31T02:14:01Z 2024 Conference Paper Guleria, Y., Pham, D. & Alam, S. (2024). Advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance. 11th International Conference on Research in Air transportation (ICRAT 2024). https://hdl.handle.net/10356/179379 en © 2024 ICRAT. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at https://www.icrat.org/upcoming-conference/papers/. application/pdf |
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Computer and Information Science Engineering Air traffic conflict resolution Machine learning Supervised learning Air traffic controllers Conformal automation Human-AI teaming |
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Computer and Information Science Engineering Air traffic conflict resolution Machine learning Supervised learning Air traffic controllers Conformal automation Human-AI teaming Guleria, Yash Pham, Duc-Thinh Alam, Sameer Advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance |
description |
While favored by air traffic controllers (ATCOs), ATCO-
conformal conflict resolution often lacks operational efficiency
due to such potential issues as excessive aircraft deviations and
higher separation buffers. This study investigated the potential of
advancing beyond conformal conflict resolution advisories in en-
route air traffic control by balancing optimality and conformance
of the advisories to bridge the gap between ATCO conformance and
maneuver efficiency. Firstly, a machine learning (ML) approach is
proposed to generate various conflict resolution advisories, includ-
ing balanced options. Then, initial human-in-the-loop experiments
with 6 experienced ATCOs were conducted, wherein ATCOs were
required to rank conflict resolution advisories based on their
preferences, for the presented conflict scenarios. Given the trade-off
between conformance and efficiency of the conflict resolution ma-
neuvers, this research investigated the ATCOs’ preferences among
the resolution options provided, for the conflict scenarios. Results
indicated a strong preference for conformal conflict resolution in
70 of 114 scenarios collected, with a significant inclination towards
balanced resolutions in 62 scenarios, suggesting a move towards
more efficient strategies without compromising conformance. Cu-
mulatively, conformal and balanced conflict resolutions were the
most favorable and second most favorable choices for 77.19% and
75.4% of the scenarios, respectively. ATCO feedback also indicated
that such a conflict resolution advisory mechanism could prove
useful in improving controller decision-making. These findings
highlighted the need for further research into adaptive conflict
resolution models that can potentially better balance the tradeoffs
between optimal- and conformal resolution models. |
author2 |
11th International Conference on Research in Air transportation (ICRAT 2024) |
author_facet |
11th International Conference on Research in Air transportation (ICRAT 2024) Guleria, Yash Pham, Duc-Thinh Alam, Sameer |
format |
Conference or Workshop Item |
author |
Guleria, Yash Pham, Duc-Thinh Alam, Sameer |
author_sort |
Guleria, Yash |
title |
Advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance |
title_short |
Advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance |
title_full |
Advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance |
title_fullStr |
Advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance |
title_full_unstemmed |
Advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance |
title_sort |
advancing beyond conformal conflict resolution in air traffic control: balancing efficiency and conformance |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/179379 |
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1814047203349495808 |