Causal effects of landing parameters on runway occupancy time using causal machine learning models

Limited runway capacity is a common problem faced by most airports worldwide. The two important factors that affect runway throughput are the wake-vortex separation and Runway Occupancy Time (ROT). Therefore, to improve runway throughput, Wake Turbulence Re-categorisation program (RECAT) was introdu...

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Main Authors: Lim, Zhi Jun, Goh, Sim Kuan, Dhief, Imen, Alam, Sameer
Other Authors: School of Mechanical and Aerospace Engineering
Format: Conference or Workshop Item
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/146537
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1465372021-02-27T20:10:20Z Causal effects of landing parameters on runway occupancy time using causal machine learning models Lim, Zhi Jun Goh, Sim Kuan Dhief, Imen Alam, Sameer School of Mechanical and Aerospace Engineering 2020 IEEE Symposium Series on Computational Intelligence (SSCI) Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Aeronautical engineering::Aviation Causal Machine Learning Runway Occupancy Time Limited runway capacity is a common problem faced by most airports worldwide. The two important factors that affect runway throughput are the wake-vortex separation and Runway Occupancy Time (ROT). Therefore, to improve runway throughput, Wake Turbulence Re-categorisation program (RECAT) was introduced to reduce the minimum separation distance required between successive aircraft on final approach. As a result, the constraining impact of ROT on runway throughput has now become significant. The objective of this paper is to identify data-driven intervention to reduce the ROT of landing aircraft. Specifically, we propose a data-driven approach to estimate the causal effect of landing parameters on ROT. We propose categorisation of each landing parameter into groups using Gaussian process models and employ Generalised Random Forest (GRF) to estimate the average treatment effect and the standard deviation of each landing parameters. Experimental results show that a few procedural changes to current landing procedure may reduce ROT. The results establish that slowing down the aircraft speed in the final approach phase leads to shorter ROT. In the final approach phase, ROTs of aircraft which are at least 10 knots slower than the average aircraft speed are on an average 2.63 seconds shorter. Furthermore, aircraft that are at least 10 knots faster than the average aircraft have on average 4 seconds longer ROTs. The second finding of this work is that flexible glide-slope angles should be introduced for the different aircraft types to achieve better ROT performance. Therefore, our findings also validate the industry need for Ground-Based Augmented System landing system which provides landing guidance with flexible glide-slopes. Civil Aviation Authority of Singapore (CAAS) Nanyang Technological University Accepted version This research has been supported by the Civil Aviation Authority of Singapore (CAAS) and the Air Traffic Management Research Institute (ATMRI), Singapore. 2021-02-25T05:11:18Z 2021-02-25T05:11:18Z 2020 Conference Paper Lim, Z. J., Goh, S. K., Dhief, I., & Alam, S. (2020). Causal effects of landing parameters on runway occupancy time using causal machine learning models. Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI), 2713-2722. doi:10.1109/SSCI47803.2020.9308243 https://hdl.handle.net/10356/146537 10.1109/SSCI47803.2020.9308243 2713 2722 en © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/SSCI47803.2020.9308243 application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Aeronautical engineering::Aviation
Causal Machine Learning
Runway Occupancy Time
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Aeronautical engineering::Aviation
Causal Machine Learning
Runway Occupancy Time
Lim, Zhi Jun
Goh, Sim Kuan
Dhief, Imen
Alam, Sameer
Causal effects of landing parameters on runway occupancy time using causal machine learning models
description Limited runway capacity is a common problem faced by most airports worldwide. The two important factors that affect runway throughput are the wake-vortex separation and Runway Occupancy Time (ROT). Therefore, to improve runway throughput, Wake Turbulence Re-categorisation program (RECAT) was introduced to reduce the minimum separation distance required between successive aircraft on final approach. As a result, the constraining impact of ROT on runway throughput has now become significant. The objective of this paper is to identify data-driven intervention to reduce the ROT of landing aircraft. Specifically, we propose a data-driven approach to estimate the causal effect of landing parameters on ROT. We propose categorisation of each landing parameter into groups using Gaussian process models and employ Generalised Random Forest (GRF) to estimate the average treatment effect and the standard deviation of each landing parameters. Experimental results show that a few procedural changes to current landing procedure may reduce ROT. The results establish that slowing down the aircraft speed in the final approach phase leads to shorter ROT. In the final approach phase, ROTs of aircraft which are at least 10 knots slower than the average aircraft speed are on an average 2.63 seconds shorter. Furthermore, aircraft that are at least 10 knots faster than the average aircraft have on average 4 seconds longer ROTs. The second finding of this work is that flexible glide-slope angles should be introduced for the different aircraft types to achieve better ROT performance. Therefore, our findings also validate the industry need for Ground-Based Augmented System landing system which provides landing guidance with flexible glide-slopes.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Lim, Zhi Jun
Goh, Sim Kuan
Dhief, Imen
Alam, Sameer
format Conference or Workshop Item
author Lim, Zhi Jun
Goh, Sim Kuan
Dhief, Imen
Alam, Sameer
author_sort Lim, Zhi Jun
title Causal effects of landing parameters on runway occupancy time using causal machine learning models
title_short Causal effects of landing parameters on runway occupancy time using causal machine learning models
title_full Causal effects of landing parameters on runway occupancy time using causal machine learning models
title_fullStr Causal effects of landing parameters on runway occupancy time using causal machine learning models
title_full_unstemmed Causal effects of landing parameters on runway occupancy time using causal machine learning models
title_sort causal effects of landing parameters on runway occupancy time using causal machine learning models
publishDate 2021
url https://hdl.handle.net/10356/146537
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