Improving the predictability of take-off times with Machine Learning : a case study for the Maastricht upper area control centre area of responsibility

The uncertainty of the take-off time is a major contribution to the loss of trajectory predictability. At present, the Estimated Take-Off Time (ETOT) for each individual flight is extracted from the Enhanced Traffic Flow Management System (ETFMS) messages, which are sent each time there is an event triggering a recalculation of the flight data by the Network Man- ager Operations Centre. However, aircraft do not always take- off at the ETOTs reported by the ETFMS due to several factors, including congestion and bad weather conditions at the departure airport, reactionary delays and air traffic flow management slot improvements. This paper presents two machine learning models that take into account several of these factors to improve the take- off time prediction of individual flights one hour before their estimated off-block time. Predictions performed by the model trained on three years of historical flight and weather data show a reduction on the take-off time prediction error of about 30% as compared to the ETOTs reported by the ETFMS.Peer ReviewedPostprint (published version

ATM performance measurement in Europe, the US and China

Air traffic management (ATM) performance and the metrics used in its assessment are investigated for the first time across the three largest ATM world regions: Europe, the US and China. The market structure and flow man-agement practices of each region are presented. A wide range of performance data across these three regions is syn-thesised. For topological and performance assessment, the notion of a ‘sufficient’ sample is often non-intuitive: many metrics may behave non-monotonically as a function of sampling fraction. Missing and under-developed metrics are identified, and the need for a balance between standardisation and flexibility is proposed. Longitudinal and cross-sectional metric trade-offs are identified

On the multi-dimensionality and sampling of air transport networks

Complex network theory is a framework increasingly used in the study of air transport networks, thanks to its ability to describe the structures created by networks of flights, and their influence in dynamical processes such as delay propagation. While many works consider only a fraction of the network, created by major airports or airlines, for example, it is not clear if and how such sampling process bias the observed structures and processes. In this contribution, we tackle this problem by studying how some observed topological metrics depend on the way the network is reconstructed, i.e. on the rules used to sample nodes and connections. Both structural and simple dynamical properties are considered, for eight major air networks and different source datasets. Results indicate that using a subset of airports strongly distorts our perception of the network, even when just small ones are discarded; at the same time, considering a subset of airlines yields a better and more stable representation. This allows us to provide some general guidelines on the way airports and connections should be sampled

Complex Networks and Data Mining : toward a new perspective for the understanding of Air Transportation

Existen muchos sistemas en el mundo real que se consideran sistemas complejos, es decir, sistemas compuestos de numerosos y diversos elementos que transportan e intercambian información de una manera no lineal. El enfoque microscópico adoptado para mejorar el entendimiento de dichos sistemas está siendo reemplazado últimamente por un planteamiento más macroscópico, es decir, por el procesamiento de la información del sistema. En otras palabras, las métricas de comportamiento resultantes de un rastreo físico e individual de los elementos del sistema están siendo abandonadas progresivamente en beneficio del estudio de la distribución, procesamiento y flujo de la información. Este nuevo enfoque tiene la importante ventaja de basarse en datos reales, sin necesidad de conocimientos previos para la construcción de modelos y, por lo tanto, sin necesidad de costosas simulaciones. El estudio del transporte aéreo en general y de la propagación de retrasos en particular, se presta perfectamente al uso de tal enfoque. Este tema tiene una alta importancia en el sector por sus consecuencias económicas y ambientales y por su relación con la seguridad del sistema, pero hasta ahora ha sido analizada casi exclusivamente desde una perspectiva microscópica. El reciente crecimiento del acceso a datos relacionados con la aviación parece favorecer un planteamiento más macroscópico. Desde nuestro punto de vista, esta tesis doctoral aborda por primera vez el estudio de la propagación de retrasos combinando la tradicional visión individual con una perspectiva más panorámica del proceso, resultando en una caracterización más completa. En concreto, el trabajo consta de tres partes. En primer lugar, se analiza el grado de subjetividad resultante de las posibles representaciones del sistema aéreo basadas en redes y cómo éstas condicionan los resultados obtenidos respecto a la propagación de retrasos. Posteriormente, se presenta la herramienta de análisis de datos creada para la extracción de relaciones causales no lineales y, por tanto, más adecuadas al problema de estudio. Finalmente, se completan los resultados con un análisis microscópico tradicional para proporcionar una visión global de proceso de propagación. Los análisis de este trabajo se han efectuado sobre datos del trafico aéreo europeo y han sido extendidos a otras regiones de acuerdo con los datos disponibles. ----------ABSTRACT---------- Complex systems, i.e. systems composed of a large set of elements transporting and interchanging information in a non-linear way, are constantly found all around us. In the last decades, the approach toward their understanding has shifted progressively from a transportation to an information processing point of view. In other words, we are moving from a movement-based analysis (i.e. tracking the movement of items through time and space to reconstruct various metrics about their behaviour) to a higher-level approach, where individual movements are left aside to focus on the distribution, processing and flow of the information within the system. The information processing approach presents the main advantage of being data-based, that is, that no a priori knowledge about the interactions in the system is needed, hence the absence of costly simulations models. Such paradigm perfectly fits within the air transport system, where thematics as important as delay propagation (for its economical, environmental and safety related consequences) has been until now mainly analysed from a transportation micro-level perspective. Yet, the progressive rise in aviation of data analyses encourages a more data-centred path. We here present the first work that aims at fostering the combined use of the intuitive microscopic point of view with a higher-level information processing approach, yielding a more complete characterisation of the delay propagation process. Specifically, the here propose a three-fold approach. First, we highlight the degree of subjectivity associated with network-based representations of the air transport system, which conditions the intelligence extracted from any information processing study. Secondly, we manufactur a new data mining technique to extract non-linear causality relationships, therefore enabling the creation of a more complete delay propagation network representation. Finally, we complement our results by a micro-level analysis, therefore ending up with a 360◦ view of the delay propagation process. These analysis have been performed mainly on a European dataset, but expanded to other airspaces whenever data have been available

Beyond Linear Delay Multipliers in Air Transport

Delays are considered one of the most important burdens of air transport, both for their social and environmental consequences and for the cost they cause for airlines and passengers. It is therefore not surprising that a large effort has been devoted to study how they propagate through the system. One of the most important indicators to assess such propagation is the delay multiplier, a ratio between outbound and inbound average delays; in spite of its widespread utilisation, its simplicity precludes capturing all details about the dynamics behind the diffusion process. Here we present a methodology that extracts a more complete relationship between the in- and outbound delays, distinguishing a linear and a nonlinear phase and thus yielding a richer description of the system’s response as a function of the delay magnitude. We validate the methodology through the study of a historical data set of flights crossing the European airspace and show how its most important airports have heterogeneous ways of reacting to extreme delays and that this reaction strongly depends on some of their global properties

Fostering interpretability of data mining models through data perturbation

With the widespread adoption of data mining models to solve real-world problems, the scientific community is facing the need of increasing their interpretability and comprehensibility. This is especially relevant in the case of black box models, in which inputs and outputs are usually connected by highly complex and nonlinear functions; in applications requiring an interaction between the user and the model; and when the machine’s solution disagrees with the human experience. In this contribution we present a new methodology that allows to simplify the process of understanding the rules behind a classification model, even in the case of black box ones. It is based on the perturbation of the features describing one instance, and on finding the minimal variation required to change the forecasted class. It thus yields simplified rules describing under which circumstances would the solution have been different, and allows to compare these with the human expectation. We show how such methodology is well defined, model-agnostic, easy to implement and modular; and demonstrate its usefulness with several synthetic and real-world data sets

Improving the predictability of take-off times with Machine Learning : a case study for the Maastricht upper area control centre area of responsibility

The uncertainty of the take-off time is a major contribution to the loss of trajectory predictability. At present, the Estimated Take-Off Time (ETOT) for each individual flight is extracted from the Enhanced Traffic Flow Management System (ETFMS) messages, which are sent each time there is an event triggering a recalculation of the flight data by the Network Man- ager Operations Centre. However, aircraft do not always take- off at the ETOTs reported by the ETFMS due to several factors, including congestion and bad weather conditions at the departure airport, reactionary delays and air traffic flow management slot improvements. This paper presents two machine learning models that take into account several of these factors to improve the take- off time prediction of individual flights one hour before their estimated off-block time. Predictions performed by the model trained on three years of historical flight and weather data show a reduction on the take-off time prediction error of about 30% as compared to the ETOTs reported by the ETFMS. Peer Reviewe

Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients

In spite of the large attention received by brain activity analyses through functional networks, the effects of uncertainty on such representations have mostly been neglected. We here elaborate the hypothesis that such uncertainty is not just a nuisance, but that on the contrary is condition-dependent. We test this hypothesis by analysing a large set of EEG brain recordings corresponding to control subjects and patients suffering from alcoholism, through the reconstruction of the corresponding Maximum Spanning Trees (MSTs), the assessment of their topological differences, and the comparison of two frequentist and Bayesian reconstruction approaches. A machine learning model demonstrates that the Bayesian reconstruction encodes more information than the frequentist one, and that such additional information is related to the uncertainty of the topological structures. We finally show how the Bayesian approach is more effective in the validation of generative models, over and above the frequentist one, by proposing and disproving two models based on additive noise.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 851255). M.Z. acknowledges the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711).Peer reviewe