Title:
Air Traffic Flow Identification and Recognition in Terminal Airspace through Machine Learning Approaches
Air Traffic Flow Identification and Recognition in Terminal Airspace through Machine Learning Approaches
| dc.contributor.author | Zhang, Wenxin | |
| dc.contributor.author | Payan, Alexia P. | |
| dc.contributor.author | Mavris, Dimitri N. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Aerospace Systems Design Laboratory | |
| dc.contributor.corporatename | American Institute of Aeronautics and Astronautics | |
| dc.date.accessioned | 2024-01-05T13:23:02Z | |
| dc.date.available | 2024-01-05T13:23:02Z | |
| dc.date.issued | 2024-01 | |
| dc.description | Presented at AIAA SciTech 2024 Forum, Intelligent Systems for Air Traffic Management Section | |
| dc.description.abstract | In modern aviation, a significant amount of data is generated during routine operations and collected using technologies like Automatic Dependent Surveillance-Broadcast (ADS-B). The abundance of such data presents great potential for utilizing emerging data analysis techniques like machine learning to enhance the future of aviation. This paper presents a methodology that leverages clustering and classification models for offline identification and online recognition of air traffic flows. This research utilizes real trajectories in the terminal area of Zurich Airport to train and assess various machine learning models. To prepare the raw trajectory data for analysis, we apply a preprocessing step to clean and resample the data. Clustering is performed using the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm, and its performance is compared to Density-Based Spatial Clustering of Applications with Noise (DBSCAN). For classification of the data, we employ two ensemble methods, Random Forest and Extreme Gradient Boosting (XGBoost), and compare their outcomes with those of Long Short-term Memory (LSTM). Our results demonstrate the superior reliability of OPTICS compared to the baseline method for clustering, and the ensemble models perform as effectively as the deep learning model, but with shorter training times due to their relative simplicity. The proposed methodology enhances the understanding of air traffic flows at specific airports and facilitates subsequent trajectory-centric tasks such as anomaly detection, trajectory prediction, and conflict detection, ultimately contributing to the improvement of safety in the terminal airspace. | |
| dc.identifier.citation | Zhang, Wenxin, Alexia P. Payan, and Dimitri N. Mavris. "Air Traffic Flow Identification and Recognition in Terminal Airspace through Machine Learning Approaches." AIAA Scitech 2024 Forum. 2024. DOI: https://doi.org/10.2514/6.2024-0536 | |
| dc.identifier.doi | https://doi.org/10.2514/6.2024-0536 | |
| dc.identifier.uri | https://hdl.handle.net/1853/72956 | |
| dc.publisher | Georgia Institute of Technology | |
| dc.publisher.original | American Institute of Aeronautics and Astronautics (AIAA) | |
| dc.rights.metadata | https://creativecommons.org/publicdomain/zero/1.0/ | |
| dc.subject | Aviation safety | |
| dc.subject | Aircraft trajectory | |
| dc.subject | Clustering | |
| dc.subject | Machine Learning | |
| dc.title | Air Traffic Flow Identification and Recognition in Terminal Airspace through Machine Learning Approaches | |
| dc.type | Text | |
| dc.type.genre | Paper | |
| dspace.entity.type | Publication | |
| local.contributor.author | Payan, Alexia P. | |
| local.contributor.author | Mavris, Dimitri N. | |
| local.contributor.corporatename | Aerospace Systems Design Laboratory (ASDL) | |
| local.contributor.corporatename | Daniel Guggenheim School of Aerospace Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isAuthorOfPublication | 955c440c-fd29-4eb9-9923-a7e13f12667e | |
| relation.isAuthorOfPublication | d355c865-c3df-4bfe-8328-24541ea04f62 | |
| relation.isOrgUnitOfPublication | a8736075-ffb0-4c28-aa40-2160181ead8c | |
| relation.isOrgUnitOfPublication | a348b767-ea7e-4789-af1f-1f1d5925fb65 | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 |
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