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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/146537 |
| _version_ | 1811692981914173440 |
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| author | Lim, Zhi Jun Goh, Sim Kuan Dhief, Imen Alam, Sameer |
| 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 |
| author_sort | Lim, Zhi Jun |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T06:44:26Z |
| format | Conference Paper |
| id | ntu-10356/146537 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:44:26Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Aeronautical engineering::Aviation Causal Machine Learning Runway Occupancy Time |
| url | https://hdl.handle.net/10356/146537 |
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