A Safety-Aware Real-Time Air Traffic Flow Management Model Under Demand and Capacity Uncertainties

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A Safety-Aware Real-Time Air Traffic Flow Management Model Under Demand and Capacity Uncertainties
Title:
A Safety-Aware Real-Time Air Traffic Flow Management Model Under Demand and Capacity Uncertainties
Other Titles:
IEEE Transactions on Intelligent Transportation Systems
Publication Date:
09 September 2021
Citation:
Sandamali, G. G. N., Su, R., Sudheera, K. L. K., & Zhang, Y. (2021). A Safety-Aware Real-Time Air Traffic Flow Management Model Under Demand and Capacity Uncertainties. IEEE Transactions on Intelligent Transportation Systems, 1–14. doi:10.1109/tits.2021.3083964
Abstract:
Inherent uncertainties of the air transportation system (ATS) can induce unexpected anomalies in its operations such as deviations in flight schedules, sudden imbalances of demands and capacities, etc.. Current air traffic flow management (ATFM) models rarely consider both demand and capacity uncertainties in their algorithms, and generally focus on minimizing the flight delays under deterministic constraints. Thus, to bridge this gap, we propose a framework for en-route ATFM while scrutinizing uncertainties in en-route capacity and demand and their imbalance, via a chance constraint based probabilistic approach. The proposed framework plays a key role in ensuring the safety of the overall ATS in terms of maintaining the safety separation between flights and constraining the capacity of the sectors as well. Moreover, flight level assignments scheme is proposed based on the Base of Aircraft Data (BADA) of the European Organization for the Safety of Air Navigation (EUROCONTROL) with the objective of minimizing the fuel consumption. The model further minimizes the overall expected delay of the system using the control actions of ground holding, speed control, rerouting, and flight cancellations. At the implementation stage, two phases of ATFM as pre-tactical and tactical are considered, in which the former focuses on generating optimal trajectories and the latter focuses on real-time updates of flight plans. The computational complexity is reduced by shrinking the feasibility region and decomposing the problem into maximum weighted independent sets. The experimental results of realistic large-scale problems demonstrate the effectiveness and computational feasibility of our ATFM framework.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 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
ISSN:
1524-9050
1558-0016
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