18 research outputs found
Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management
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.Comment: Interdisciplinary Science for Innovative Air Traffic Management
(2013
Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management
We investigate a method to deal with congestion of sectors and delays in the
tactical phase of air traffic flow and capacity management. It relies on
temporal objectives given for every point of the flight plans and shared among
the controllers in order to create a collaborative environment. This would
enhance the transition from the network view of the flow management to the
local view of air traffic control. Uncertainty is modeled at the trajectory
level with temporal information on the boundary points of the crossed sectors
and then, we infer the probabilistic occupancy count. Therefore, we can model
the accuracy of the trajectory prediction in the optimization process in order
to fix some safety margins. On the one hand, more accurate is our prediction;
more efficient will be the proposed solutions, because of the tighter safety
margins. On the other hand, when uncertainty is not negligible, the proposed
solutions will be more robust to disruptions. Furthermore, a multiobjective
algorithm is used to find the tradeoff between the delays and congestion, which
are antagonist in airspace with high traffic density. The flow management
position can choose manually, or automatically with a preference-based
algorithm, the adequate solution. This method is tested against two instances,
one with 10 flights and 5 sectors and one with 300 flights and 16 sectors.Comment: IEEE Congress on Evolutionary Computation (2013). arXiv admin note:
substantial text overlap with arXiv:1309.391
Online Learning for Ground Trajectory Prediction
This paper presents a model based on an hybrid system to numerically simulate
the climbing phase of an aircraft. This model is then used within a trajectory
prediction tool. Finally, the Covariance Matrix Adaptation Evolution Strategy
(CMA-ES) optimization algorithm is used to tune five selected parameters, and
thus improve the accuracy of the model. Incorporated within a trajectory
prediction tool, this model can be used to derive the order of magnitude of the
prediction error over time, and thus the domain of validity of the trajectory
prediction. A first validation experiment of the proposed model is based on the
errors along time for a one-time trajectory prediction at the take off of the
flight with respect to the default values of the theoretical BADA model. This
experiment, assuming complete information, also shows the limit of the model. A
second experiment part presents an on-line trajectory prediction, in which the
prediction is continuously updated based on the current aircraft position. This
approach raises several issues, for which improvements of the basic model are
proposed, and the resulting trajectory prediction tool shows statistically
significantly more accurate results than those of the default model.Comment: SESAR 2nd Innovation Days (2012
Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management
International audienceWe investigate a method to deal with congestion of sectors and delays in the tactical phase of air traffic flow and capacity management. It relies on temporal objectives given for every point of the flight plans and shared among the controllers in order to create a collaborative environment. This would enhance the transition from the network view of the flow management to the local view of air traffic control. Uncertainty is modeled at the trajectory level with temporal information on the boundary points of the crossed sectors and then, we infer the probabilistic occupancy count. Therefore, we can model the accuracy of the trajectory prediction in the optimization process in order to fix some safety margins. On the one hand, more accurate is our prediction; more efficient will be the proposed solutions, because of the tighter safety margins. On the other hand, when uncertainty is not negligible, the proposed solutions will be more robust to disruptions. Furthermore, a multiobjective algorithm is used to find the tradeoff between the delays and congestion, which are antagonist in airspace with high traffic density. The flow management position can choose manually, or automatically with a preference-based algorithm, the adequate solution. This method is tested against two instances, one with 10 flights and 5 sectors and one with 300 flights and 16 sectors
Practical Riemannian Neural Networks
We provide the first experimental results on non-synthetic datasets for the quasi-diagonal Riemannian gradient descents for neural networks introduced in [Ollivier, 2015]. These include the MNIST, SVHN, and FACE datasets as well as a previously unpublished electroencephalogram dataset. The quasi-diagonal Riemannian algorithms consistently beat simple stochastic gradient gradient descents by a varying margin. The computational overhead with respect to simple backpropagation is around a factor . Perhaps more interestingly, these methods also reach their final performance quickly, thus requiring fewer training epochs and a smaller total computation time. We also present an implementation guide to these Riemannian gradient descents for neural networks, showing how the quasi-diagonal versions can be implemented with minimal effort on top of existing routines which compute gradients
Author manuscript, published in "ATM Seminar 2013 (2013)" Strategic Planning in Air Traffic Control as a Multi-objective Stochastic Optimization Problem Gaétan Marceau
Abstract—With the objective of handling the airspace sector congestion subject to continuously growing air traffic, we suggest to create a collaborative working plan during the strategic phase of air traffic control. The plan obtained via a new decisionsupport tool presented in this article consists in a schedule for controllers, which specifies time of overflight on the different waypoints of the flight plans. In order to do it, we believe that the decision-support tool shall model directly the uncertainty at a trajectory level in order to propagate the uncertainty to the sector level. Then, the probability of congestion for any sector in the airspace can be computed. Since air traffic regulations and sector congestion are antagonist, we designed and implemented a multi-objective optimization algorithm for determining the best trade-off between these two criteria. The solution comes up as a set of alternatives for the multi-sector planner where the severity of the congestion cost is adjustable. In this paper, the Nondominated Sorting Genetic Algorithm (NSGA-II) was used to solve an artificial benchmark problem involving 24 aircraft and 11 sectors, and is able to provide a good approximation of the Pareto front