A data-driven framework for modelling complexity in terminal manoeuvring area
This paper presents an objective, data-driven framework for quantifying air traffic complexity in the Terminal Manoeuvring Area (TMA) using historical ADS-B data from Singapore TMA. The motivation for developing this framework stems from the limitations of traditional subjective measures, which are...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/181825 https://www.sesarju.eu/SIDS2024 |
| _version_ | 1826115686656638976 |
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| author | Lim, Zhi Jun Dhief, Imen Pham, Duc-Thinh Alam, Sameer Delahaye, Daniel |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Lim, Zhi Jun Dhief, Imen Pham, Duc-Thinh Alam, Sameer Delahaye, Daniel |
| author_sort | Lim, Zhi Jun |
| collection | NTU |
| description | This paper presents an objective, data-driven framework for quantifying air traffic complexity in the Terminal Manoeuvring Area (TMA) using historical ADS-B data from Singapore TMA. The motivation for developing this framework stems from the limitations of traditional subjective measures, which are often influenced by individual perceptions and can vary significantly between air traffic controllers. Subjective measures may also fail to capture real-time operational demands, especially in complex, high-density environments such as Singapore TMA. By focusing on operational outcomes—specifically vectoring and holding patterns—the framework provides a more accurate reflection of real-time complexity. Principal Component Analysis (PCA) and k-means clustering are employed to classify complexity levels based on trajectory features such as arc lengths, curvatures, and holding durations. The results show that total arc lengths and curvatures are significant complexity factors, with extensive vectoring contributing more to TMA complexity than holding patterns. The significance of this work lies in its data-driven and objective approach to measuring air traffic complexity, offering a more accurate reflection of real-time demands compared to traditional subjective methods. Quantitative evaluations across multiple real-world scenarios validate the framework's effectiveness, showing that TMA complexity is more strongly associated with vectoring intensity and holding patterns than with flight density alone. This current framework can be extended to incorporate vertical profiles of arrival and departure flights and develop predictive models with practical, actionable lookahead times for real-time air traffic management. |
| first_indexed | 2025-03-09T11:27:09Z |
| format | Conference Paper |
| id | ntu-10356/181825 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T11:27:09Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1818252024-12-31T15:31:01Z A data-driven framework for modelling complexity in terminal manoeuvring area Lim, Zhi Jun Dhief, Imen Pham, Duc-Thinh Alam, Sameer Delahaye, Daniel School of Mechanical and Aerospace Engineering SESAR Innovation Days 2024 Air Traffic Management Research Institute Computer and Information Science Engineering Air traffic management Terminal manoeuvring area Complexity Data-driven Unsupervised machine learning Clustering This paper presents an objective, data-driven framework for quantifying air traffic complexity in the Terminal Manoeuvring Area (TMA) using historical ADS-B data from Singapore TMA. The motivation for developing this framework stems from the limitations of traditional subjective measures, which are often influenced by individual perceptions and can vary significantly between air traffic controllers. Subjective measures may also fail to capture real-time operational demands, especially in complex, high-density environments such as Singapore TMA. By focusing on operational outcomes—specifically vectoring and holding patterns—the framework provides a more accurate reflection of real-time complexity. Principal Component Analysis (PCA) and k-means clustering are employed to classify complexity levels based on trajectory features such as arc lengths, curvatures, and holding durations. The results show that total arc lengths and curvatures are significant complexity factors, with extensive vectoring contributing more to TMA complexity than holding patterns. The significance of this work lies in its data-driven and objective approach to measuring air traffic complexity, offering a more accurate reflection of real-time demands compared to traditional subjective methods. Quantitative evaluations across multiple real-world scenarios validate the framework's effectiveness, showing that TMA complexity is more strongly associated with vectoring intensity and holding patterns than with flight density alone. This current framework can be extended to incorporate vertical profiles of arrival and departure flights and develop predictive models with practical, actionable lookahead times for real-time air traffic management. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Published version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. 2024-12-26T07:42:44Z 2024-12-26T07:42:44Z 2024 Conference Paper Lim, Z. J., Dhief, I., Pham, D., Alam, S. & Delahaye, D. (2024). A data-driven framework for modelling complexity in terminal manoeuvring area. SESAR Innovation Days 2024, 2024-081. https://hdl.handle.net/10356/181825 https://www.sesarju.eu/SIDS2024 2024-081 en © 2024 SESAR 3 Joint Undertaking. 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.sesarju.eu/SIDS2024. application/pdf |
| spellingShingle | Computer and Information Science Engineering Air traffic management Terminal manoeuvring area Complexity Data-driven Unsupervised machine learning Clustering Lim, Zhi Jun Dhief, Imen Pham, Duc-Thinh Alam, Sameer Delahaye, Daniel A data-driven framework for modelling complexity in terminal manoeuvring area |
| title | A data-driven framework for modelling complexity in terminal manoeuvring area |
| title_full | A data-driven framework for modelling complexity in terminal manoeuvring area |
| title_fullStr | A data-driven framework for modelling complexity in terminal manoeuvring area |
| title_full_unstemmed | A data-driven framework for modelling complexity in terminal manoeuvring area |
| title_short | A data-driven framework for modelling complexity in terminal manoeuvring area |
| title_sort | data driven framework for modelling complexity in terminal manoeuvring area |
| topic | Computer and Information Science Engineering Air traffic management Terminal manoeuvring area Complexity Data-driven Unsupervised machine learning Clustering |
| url | https://hdl.handle.net/10356/181825 https://www.sesarju.eu/SIDS2024 |
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