Ph.D
Group : Learning and Optimization

Optimization and Uncertainty Handling in Air Traffic Management

Starts on 14/12/2010
Advisor : SCHOENAUER, Marc

Funding : Convention industrielle de formation par la recherche
Affiliation : Université Paris-Saclay
Laboratory : LRI INRIA

Defended on 22/09/2014, committee :

Research activities :

Abstract :
In this thesis, we investigate the issue of optimising the aircraft operators' demand with the airspace capacity by taking into account uncertainty in air traffic management.

In the first part of the work, we identify the main causes of uncertainty of the trajectory prediction (TP), the core component underlying automation in ATM systems.

We study the problem of online parameter-tuning of the TP during the climbing phase with the optimization algorithm CMA-ES.

The main conclusion, corroborated by other works in the literature, is that ground TP is not sufficiently accurate nowadays to support fully automated safety-critical applications.

Hence, with the current data sharing limitations, any centralized optimization system in Air Traffic Control should consider the human-in-the-loop factor, as well as other uncertainties.

Consequently, in the second part of the thesis, we develop models and algorithms from a network global perspective and we describe a generic uncertainty model that captures flight trajectory uncertainties and infer their impact on the occupancy count of the Air Traffic Control sectors.

This usual indicator quantifies coarsely the complexity managed by air traffic controllers in terms of number of flights.

In the third part of the thesis, we formulate a variant of the Air Traffic Flow and Capacity Management problem in the tactical phase for bridging the gap between the network manager and air traffic controllers.

The optimization problem consists in minimizing jointly the cost of delays and the cost of congestion while meeting sequencing constraints.

In order to cope with the high dimensionality of the problem, evolutionary multi-objective optimization algorithms are used with an indirect representation and some greedy schedulers to optimize flight plans.

An additional uncertainty model is added on top of the network model, allowing us to study the performances and the robustness of the proposed optimization algorithm when facing noisy context.

We validate our approach on real-world and artificially densified instances obtained from the Central Flow Management Unit in Europe.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.