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