Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management
This work addresses computational challenges in air traffic management for realistic simulations, though it appears incremental as it compares existing methods on a specific model.
The paper tackles the problem of computing expected cost functions from a probabilistic model of air traffic flow and capacity management, comparing Clenshaw-Curtis quadrature to tailored Monte-Carlo algorithms. The results show that both approaches have comparable performance for computing expected costs of delay and congestion, with Monte-Carlo being more sensitive to uncertainty but providing on-demand accuracy.
This article addresses the issue of computing the expected cost functions from a probabilistic model of the air traffic flow and capacity management. The Clenshaw-Curtis quadrature is compared to Monte-Carlo algorithms defined specifically for this problem. By tailoring the algorithms to this model, we reduce the computational burden in order to simulate real instances. The study shows that the Monte-Carlo algorithm is more sensible to the amount of uncertainty in the system, but has the advantage to return a result with the associated accuracy on demand. The performances for both approaches are comparable for the computation of the expected cost of delay and the expected cost of congestion. Finally, this study shows some evidences that the simulation of the proposed probabilistic model is tractable for realistic instances.