Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144461
Title: A map-matching algorithm for ground movement trajectory representation using A-SMGCS data
Authors: Tran, Thanh-Nam
Pham, Duc-Thinh
Alam, Sameer
Keywords: Engineering::Aeronautical engineering
Issue Date: 2020
Source: 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
Project: M4062429.052 
Abstract: 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.
URI: https://hdl.handle.net/10356/144461
ISBN: 978-1-7281-5381-0
DOI: 10.1109/AIDA-AT48540.2020.9049181
Rights: © 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
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers

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