Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162778
Title: A multi-task learning approach for facilitating dynamic airspace sectorization
Authors: Zhou, Wei
Cai, Qing
Alam, Sameer
Keywords: Engineering::Aeronautical engineering
Issue Date: 2022
Source: Zhou, W., Cai, Q. & Alam, S. (2022). A multi-task learning approach for facilitating dynamic airspace sectorization. International Workshop on ATM/CNS (IWAC 2022), 192-199. https://dx.doi.org/10.57358/iwac.1.0_192
Abstract: Dynamic Airspace Sectorization (DAS) is a key pathway for enabling advanced demand capacity balancing (DCB) in modernizing Air Traffic Management (ATM). By splitting and merging the sectors, DAS allows airspace to accommodate the evolving air traffic situations for improving the utilization of airspace in response to different air traffic demands, airspace capacity, weather events and other factors. This research aims at supporting the decision-making on when-to-do such DAS from a deep learning perspective. To this end, this paper proposes a multi-task learning (MTL) approach which is able to predict sector traffic flow and airspace capacity simultaneously using a shared neural network architecture. Specifically, the proposed model predicts the demand-capacity imbalance and identifies the opportunity for sector split/merge implementation. To validate the feasibility of the proposed model, a case study has been carried out in Singapore en-route airspace using the Automatic Dependent Surveillance – Broadcast (ADS-B) data and meteorology data in December 2019. Experimental results explicitly show the capability of the proposed MTL model in predicting flow and capacity. Based on predicted results along with a pre-defined rule, the proposed model predicts the demand-capacity imbalance across multiple timescales and explores the potential to facilitate DAS in terms of tactic, pre-tactic and strategic ATM operations.
URI: https://hdl.handle.net/10356/162778
URL: https://www.jstage.jst.go.jp/browse/iwac/list/-char/en
DOI: 10.57358/iwac.1.0_192
Rights: © 2022 Electronic Navigation Research Institute (ENRI). All rights reserved. This paper was published in the Proceedings of International Workshop on ATM/CNS (IWAC 2022) and is made available with permission of Electronic Navigation Research Institute (ENRI).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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