Intelligent departure metering assistant tool (IDMAT) for airside congestion management
Airport Departure Metering (DM) is an effective approach to contain taxi delays by controlling departure pushback timings. In this work, we demonstrate the potential of Deep Reinforcement Learning (DRL) based DM method to reduce taxi delays by effectively transferring delays from taxiways to gates....
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/162187 |
| _version_ | 1824454550334996480 |
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| author | Ali, Hasnain Pham, Duc-Thinh Tran, Thanh-Nam Alam, Sameer |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Ali, Hasnain Pham, Duc-Thinh Tran, Thanh-Nam Alam, Sameer |
| author_sort | Ali, Hasnain |
| collection | NTU |
| description | Airport Departure Metering (DM) is an effective approach to contain taxi delays by controlling departure pushback timings. In this work, we demonstrate the potential of Deep Reinforcement Learning (DRL) based DM method to reduce taxi delays by effectively transferring delays from taxiways to gates. This work casts the DM problem in a markov decision process framework to train a DM policy over simulations generated using historical airport surface movement data. We further develop an Intelligent Departure Metering Assistant Tool (IDMAT) that employs the trained DM policy to recommend pushback advisories to Air Traffic Controller (ATCO). Furthermore, to assist pushback approval decisions, IDMAT displays additional traffic information like potential downstream conflicts, runway queues, and delay evolution on the taxiway to ATCOs. We intend to perform validation experiments with ATCOs to evaluate the efficacy and acceptability of the recommended pushback advisories. Similar scenarios---with and without pushback advisories and additional traffic information---shall be presented to ATCOs to evaluate the ATCO performance while managing congestion. ATCO actions (advisory accept/reject) shall also be fed-back to train the DM policy into recommending ATCO-like actions. |
| first_indexed | 2025-02-19T03:24:06Z |
| format | Conference Paper |
| id | ntu-10356/162187 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:24:06Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1621872023-01-07T23:30:24Z Intelligent departure metering assistant tool (IDMAT) for airside congestion management Ali, Hasnain Pham, Duc-Thinh Tran, Thanh-Nam Alam, Sameer School of Mechanical and Aerospace Engineering 12th SESAR Innovation Days (SIDs 2022) Air Traffic Management Research Institute Engineering::Computer science and engineering::Information systems::Information interfaces and presentation Airport Departure Metering Deep Reinforcement Learning Intelligent Pushback Control Decision Making Under Uncertainty Airport Departure Metering (DM) is an effective approach to contain taxi delays by controlling departure pushback timings. In this work, we demonstrate the potential of Deep Reinforcement Learning (DRL) based DM method to reduce taxi delays by effectively transferring delays from taxiways to gates. This work casts the DM problem in a markov decision process framework to train a DM policy over simulations generated using historical airport surface movement data. We further develop an Intelligent Departure Metering Assistant Tool (IDMAT) that employs the trained DM policy to recommend pushback advisories to Air Traffic Controller (ATCO). Furthermore, to assist pushback approval decisions, IDMAT displays additional traffic information like potential downstream conflicts, runway queues, and delay evolution on the taxiway to ATCOs. We intend to perform validation experiments with ATCOs to evaluate the efficacy and acceptability of the recommended pushback advisories. Similar scenarios---with and without pushback advisories and additional traffic information---shall be presented to ATCOs to evaluate the ATCO performance while managing congestion. ATCO actions (advisory accept/reject) shall also be fed-back to train the DM policy into recommending ATCO-like actions. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme, 2023-01-03T06:55:02Z 2023-01-03T06:55:02Z 2022 Conference Paper Ali, H., Pham, D., Tran, T. & Alam, S. (2022). Intelligent departure metering assistant tool (IDMAT) for airside congestion management. 12th SESAR Innovation Days (SIDs 2022). https://hdl.handle.net/10356/162187 en © 2022 SESAR 3 Joint Undertaking. All rights reserved. This paper was published in Proceedings of 12th SESAR Innovation Days (SIDs 2022) and is made available with permission of SESAR 3 Joint Undertaking. application/pdf |
| spellingShingle | Engineering::Computer science and engineering::Information systems::Information interfaces and presentation Airport Departure Metering Deep Reinforcement Learning Intelligent Pushback Control Decision Making Under Uncertainty Ali, Hasnain Pham, Duc-Thinh Tran, Thanh-Nam Alam, Sameer Intelligent departure metering assistant tool (IDMAT) for airside congestion management |
| title | Intelligent departure metering assistant tool (IDMAT) for airside congestion management |
| title_full | Intelligent departure metering assistant tool (IDMAT) for airside congestion management |
| title_fullStr | Intelligent departure metering assistant tool (IDMAT) for airside congestion management |
| title_full_unstemmed | Intelligent departure metering assistant tool (IDMAT) for airside congestion management |
| title_short | Intelligent departure metering assistant tool (IDMAT) for airside congestion management |
| title_sort | intelligent departure metering assistant tool idmat for airside congestion management |
| topic | Engineering::Computer science and engineering::Information systems::Information interfaces and presentation Airport Departure Metering Deep Reinforcement Learning Intelligent Pushback Control Decision Making Under Uncertainty |
| url | https://hdl.handle.net/10356/162187 |
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