dc.contributor.author | Li, B | |
dc.contributor.author | Du, W | |
dc.contributor.author | Zhang, Y | |
dc.contributor.author | Chen, J | |
dc.contributor.author | Tang, K | |
dc.contributor.author | Cao, X | |
dc.date.accessioned | 2021-09-22T08:35:30Z | |
dc.date.available | 2021-09-22T08:35:30Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 1524-9050 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/74205 | |
dc.description.abstract | Airspace complexity is a critical metric in current Air Traffic Management systems for indicating the security degree of airspace operations. Airspace complexity can be affected by many coupling factors in a complicated and nonlinear way, making it extremely difficult to be evaluated. In recent years, machine learning has been proved as a promising approach and achieved significant results in evaluating airspace complexity. However, existing machine learning based approaches require a large number of airspace operational data labeled by experts. Due to the high cost in labeling the operational data and the dynamical nature of the airspace operating environment, such data are often limited and may not be suitable for the changing airspace situation. In light of these, we propose a novel unsupervised learning approach for airspace complexity evaluation based on a deep neural network trained by unlabeled samples. We introduce a new loss function to better address the characteristics pertaining to airspace complexity data, including dimension coupling, category imbalance, and overlapped boundaries. Due to these characteristics, the generalization ability of existing unsupervised models is adversely impacted. The proposed approach is validated through extensive experiments based on the real-world data of six sectors in Southwestern China airspace. Experimental results show that our deep unsupervised model outperforms the state-of-the-art methods in terms of airspace complexity evaluation accuracy. | en_US |
dc.format.extent | 1 - 13 | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | IEEE Transactions on Intelligent Transportation Systems | |
dc.title | A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2021 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. | |
dc.identifier.doi | 10.1109/tits.2021.3106779 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing::Engineering and Physical Sciences Research Council | en_US |
qmul.funder | TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing::Engineering and Physical Sciences Research Council | en_US |