A map-matching algorithm for ground movement trajectory representation using A-SMGCS data
Increasing availability of air traffic data has opened new opportunities for better understanding of Air Traffic Management (ATM) system. At Airport-Air side, A-SMGCS (Advanced Surface Movement Guidance \& Control System) data may provide useful insights to improve efficiency and safety of airpo...
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sg-ntu-dr.10356-1444612020-11-07T20:10:26Z A map-matching algorithm for ground movement trajectory representation using A-SMGCS data Tran, Thanh-Nam Pham, Duc-Thinh Alam, Sameer School of Mechanical and Aerospace Engineering 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Air Traffic Management Research Institute Engineering::Aeronautical engineering Airport Surface Movement Map-matching Increasing availability of air traffic data has opened new opportunities for better understanding of Air Traffic Management (ATM) system. At Airport-Air side, A-SMGCS (Advanced Surface Movement Guidance \& Control System) data may provide useful insights to improve efficiency and safety of airport operations by understanding traffic patterns, taxi-way usage, ground speed profiles and any anomaly behaviour. However, A-SMGCS data comes from the fusion of several sensors such as MLAT, ADS-B and SMR. This leads to high and variable noise, missing data values, and temporal and spatial misalignment. In this study, we proposed a new and simplified representation of ground movement trajectories using a map-matching algorithm applied on A-SMGCS data. The proposed approach not only overcomes above mentioned issues of data, but also takes into consideration airport specific operational constraints. The algorithm shows a good matching results with mean percentage error of approximate 8.13\% . The matching trajectories and sequences of nodes in resulting graph, supports a variety of analysis about airport operations. To show the effectiveness of proposed approach, we performed some analysis such as traffic patterns, taxi-way usages, speed profiling and anomaly detection, using one month of A-SMGCS data at Singapore Changi Airport. Civil Aviation Authority of Singapore (CAAS) Accepted version This research / project* is supported by the Civil Aviation Authority of Singapore and Nanyang Technological University, Singapore under their collaboration in the Air Traffic Management Research Institute. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Civil Aviation Authority of Singapore. 2020-11-06T03:06:48Z 2020-11-06T03:06:48Z 2020 Conference Paper Tran, T.-N., Pham, D.-T., & Alam, S. (2020). A map-matching algorithm for ground movement trajectory representation using A-SMGCS data. Proceedings of the 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT), 1-8. doi:10.1109/AIDA-AT48540.2020.9049181 978-1-7281-5381-0 https://hdl.handle.net/10356/144461 10.1109/AIDA-AT48540.2020.9049181 en M4062429.052 © 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 is available at: https://doi.org/10.1109/AIDA-AT48540.2020.9049181 application/pdf |
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Engineering::Aeronautical engineering Airport Surface Movement Map-matching Tran, Thanh-Nam Pham, Duc-Thinh Alam, Sameer A map-matching algorithm for ground movement trajectory representation using A-SMGCS data |
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Increasing availability of air traffic data has opened new opportunities for better understanding of Air Traffic Management (ATM) system. At Airport-Air side, A-SMGCS (Advanced Surface Movement Guidance \& Control System) data may provide useful insights to improve efficiency and safety of airport operations by understanding traffic patterns, taxi-way usage, ground speed profiles and any anomaly behaviour. However, A-SMGCS data comes from the fusion of several sensors such as MLAT, ADS-B and SMR. This leads to high and variable noise, missing data values, and temporal and spatial misalignment. In this study, we proposed a new and simplified representation of ground movement trajectories using a map-matching algorithm applied on A-SMGCS data. The proposed approach not only overcomes above mentioned issues of data, but also takes into consideration airport specific operational constraints. The algorithm shows a good matching results with mean percentage error of approximate 8.13\% . The matching trajectories and sequences of nodes in resulting graph, supports a variety of analysis about airport operations. To show the effectiveness of proposed approach, we performed some analysis such as traffic patterns, taxi-way usages, speed profiling and anomaly detection, using one month of A-SMGCS data at Singapore Changi Airport. |
author2 |
School of Mechanical and Aerospace Engineering |
author_facet |
School of Mechanical and Aerospace Engineering Tran, Thanh-Nam Pham, Duc-Thinh Alam, Sameer |
format |
Conference or Workshop Item |
author |
Tran, Thanh-Nam Pham, Duc-Thinh Alam, Sameer |
author_sort |
Tran, Thanh-Nam |
title |
A map-matching algorithm for ground movement trajectory representation using A-SMGCS data |
title_short |
A map-matching algorithm for ground movement trajectory representation using A-SMGCS data |
title_full |
A map-matching algorithm for ground movement trajectory representation using A-SMGCS data |
title_fullStr |
A map-matching algorithm for ground movement trajectory representation using A-SMGCS data |
title_full_unstemmed |
A map-matching algorithm for ground movement trajectory representation using A-SMGCS data |
title_sort |
map-matching algorithm for ground movement trajectory representation using a-smgcs data |
publishDate |
2020 |
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https://hdl.handle.net/10356/144461 |
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1686109373626056704 |