AIRSPACE PLANNING FOR OPTIMAL CAPACITY, EFFICIENCY, AND SAFETY USING ANALYTICS

Air Navigation Service Providers (ANSP) worldwide have been making a considerable effort for the development of a better method for planning optimal airspace capacity, efficiency, and safety. These goals require separation and sequencing of aircraft before they depart. Prior approaches have tactically achieved these goals to some extent. However, dealing with increasingly congested airspace and new environmental factors with high levels of uncertainty still remains the challenge when deterministic approach is used. Hence due to the nature of uncertainties, we take a stochastic approach and propose a suite of analytics models for (1) Flight Time Prediction, (2) Aircraft Trajectory Clustering, (3) Aircraft Trajectory Prediction, and (4) Aircraft Conflict Detection and Resolution long before aircraft depart. The suite of data-driven models runs on a scalable Data Management System that continuously processes streaming massive flight data to achieve the strategic airspace planning for optimal capacity, efficiency, and safety. (1) Flight Time Prediction. Unlike other systems that collect and use features only for the arrival airport to build a data-driven model for predicting flight times, we use a richer set of features along the potential route, such as weather parameters and air traffic data in addition to those that are particular to the arrival airport. Our feature engineering process generates an extensive set of multidimensional time series data which goes through Time Series Clustering with Dynamic Time Warping (DTW) to generate a single set of representative features at each time instance. The features are fed into various regression and deep learning models and the best performing models with most accurate ETA predictions are selected. Evaluations on extensive set of real trajectory, weather, and airport data in Europe verify our prediction system generates more accurate ETAs with far less variance than those of European ANSP, EUROCONTROL’s. This translates to more accurately predicted flight arrival times, enabling airlines to make more cost-effective ground resource allocation and ANSPs to make more efficient flight scheduling. (2) Aircraft Trajectory Clustering. The novel divide-cluster-merge; DICLERGE system clusters aircraft trajectories by dividing them into the three standard major flight phases: climb, en-route, and descent. Trajectory segments in each phase are clustered in isolation, then merged together. Our unique approach also discovers a representative trajectory, the model for the entire trajectory set. (3) Aircraft Trajectory Prediction. Our approach considers airspace as a 3D grid network, where each grid point is a location of a weather observation. We hypothetically build cubes around these grid points, so the entire airspace can be considered as a set of cubes. Each cube is defined by its centroid, the original grid point, and associated weather parameters that remain homogeneous within the cube during a period of time. Then, we align raw trajectories to a set of cube centroids which are basically fixed 3D positions independent of trajectory data. This creates a new form of trajectories which are 4D joint cubes, where each cube is a segment that is associated with not only spatio-temporal attributes but also with weather parameters. Next, we exploit machine learning techniques to train inference models from historical data and apply a stochastic model, a Hidden Markov Model (HMM), to predict trajectories taking environmental uncertainties into account. During the process, we apply time series clustering to generate input observations from an excessive set of weather parameters to feed into the Viterbi algorithm. The experiments use a real trajectory dataset with pertaining weather observations and demonstrate the effectiveness of our approach to the trajectory prediction process for Air Traffic Management. (4) Aircraft Conflict Detection. We propose a novel data-driven system to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the system declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The system resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the system learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the system avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict-free. The key concept upon which the system is built is the assumption that the airspace is nothing more than a horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate the system using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR. Overall, in this thesis, we develop a suite of analytics models and algorithms to accurately identify current patterns in the massive flight data and use these patterns to predict future behaviors in the airspace. Upon prediction of a non-ideal outcome, we prescribe a solution to plan airspace for optimal capacity, efficiency, and safety

LSTM-based Flight Trajectory Prediction

© 2018 IEEE. Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide instructions for safely traveling. Most trajectory prediction algorithms work for land traffic, which rely on points of interest (POIs) and are only suitable for stationary road condition. Compared with land traffic prediction, flight trajectory prediction is very difficult because way-points are sparse and the flight envelopes are heavily affected by external factors. In this paper, we propose a flight trajectory prediction model based on a Long Short-Term Memory (LSTM) network. The four interacting layers of a repeating module in an LSTM enables it to connect the long-term dependencies to present predicting task. Applying sliding windows in LSTM maintains the continuity and avoids compromising the dynamic dependencies of adjacent states in the long-term sequences, which helps to improve accuracy of trajectory prediction. Taking time dimension into consideration, both 3-D (time stamp, latitude and longitude) and 4-D (time stamp, latitude, longitude and altitude) trajectories are predicted to prove the efficiency of our approach. The dataset we use was collected by ADS-B ground stations. We evaluate our model by widely used measurements, such as the mean absolute error (MAE), the mean relative error (MRE), the root mean square error (RMSE) and the dynamic warping time (DWT) methods. As Markov Model is the most popular in time series processing, comparisons among Markov Model (MM), weighted Markov Model (wMM) and our model are presented. Our model outperforms the existing models (MM and wMM) and provides a strong basis for abnormal detection and decision-making

Big data analytics for time critical maritime and aerial mobility forecasting

The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics

Machine learning for aircraft trajectory prediction: a solution for pre-tactical air traffic flow management

Pla de Doctorats Industrials de la Generalitat de Catalunya(English) The goal of air traffic flow and capacity management (ATFCM) is to ensure that airport and airspace capacity meet traffic demand while optimising traffic flows to avoid exceeding the available capacity when it cannot be further increased. In Europe, ATFCM is handled by EUROCONTROL, in its role of Network Manager (NM), and comprises three phases: strategic, pre-tactical, and tactical. This thesis is focused on the pre-tactical phase, which covers the six days prior to the day of operations. During the pre-tactical phase, few or no flight plans (FPLs) have been filed by airspace users (AUs) and the only flight information available to the NM are the so-called flight intentions (FIs), consisting mainly of flight schedules. Trajectory information becomes available only when the AUs send their FPLs. This information is required to ensure a correct allocation of resources in coordination with air navigation service providers (ANSPs). To forecast FPLs before they are filed by the AUs, the NM relies on the PREDICT tool, which generates traffic forecasts for the whole European Civil Aviation Conference (ECAC) area according to the trajectories chosen by the same or similar flights in the recent past, without taking advantage of the information on AU choices encoded in historical data. The goal of the present PhD thesis is to develop a solution for pre-tactical traffic forecast that improves the predictive performance of the PREDICT tool while being able to cope with the entire set of flights in the ECAC network in a computationally efficient manner. To this end, trajectory forecasting approaches based on machine learning models trained on historical data have been explored, evaluating their predictive performance. In the application of machine learning techniques to trajectory prediction, three fundamental methodological choices have to be made: (i) approach to trajectory clustering, which is used to group similar trajectories in order to simplify the trajectory prediction problem; (ii) model formulation; and (iii) model training approach. The contribution of this PhD thesis to the state of the-art lies in the first two areas. First, we have developed a novel route clustering technique based on the area comprised between two routes that reduces the required computational time and increases the scalability with respect to other clustering techniques described in the literature. Second, we have developed, tested and evaluated two new modelling approaches for route prediction. The first approach consists in building and training an independent machine learning model for each origin destination (OD) pair in the network, taking as inputs different variables available from FIs plus other variables related to weather and to the number of regulations. This approach improves the performance of the PREDICT model, but it also has an important limitation: it does not consider changes in the airspace structure, thus being unable to predict routes not available in the training data and sometimes predicting routes that are not compatible with the airspace structure. The second approach is an airline-based approach, which consists in building and training a model for each airline. The limitations of the first model are overcome by considering as input variables not only the variables available from the FIs and the weather, but also airspace restrictions and route characteristics (e.g., route cost, length, etc.). The airline-based approach yields a significant improvement with respect to PREDICT and to the OD pair-based model, achieving a route prediction accuracy of 0.896 (versus PREDICT’s accuracy of 0.828), while being able to deal with the full ECAC network within reasonable computational time. These promising results encourage us to be optimistic about the future implementation of the proposed system.(Català) L’objectiu de la gestió de demanda i capacitat de trànsit aeri (ATFCM per les sigles en anglès) és garantir que la capacitat aeroportuària i de l’espai aeri satisfacin la demanda de trànsit mentre s’optimitzen els fluxos per evitar excedir la capacitat disponible quan aquesta no es pot augmentar més. A Europa, l’ATFCM està a càrrec d’EUROCONTROL, i consta de tres fases: estratègica, pre-tàctica i tàctica. Aquesta tesi se centra en la pre-tàctica, que inclou els sis dies previs al dia d’operacions. Durant la fase pre-tàctica, els de l'espai aeri han presentat pocs o cap pla de vol i l’única informació sobre els vols disponible són els anomenats intencions de vol (principalment els horaris). La informació de la trajectòria només està disponible quan els usuaris envien els seus pla. Aquesta informació és necessària per assegurar una assignació correcta de recursos en coordinació amb els proveïdors de serveis de. Per predir els plans abans que siguin presentats, EUROCONTROL es recolza en l'eina PREDICT, que genera prediccions de trànsit d'acord amb les trajectòries escollides per vols similars el passat recent, sense aprofitar la informació sobre les decisions en dades històriques. L'objectiu de la present tesi doctoral és millorar l'exercici predictiu de l'eina PREDICT mitjançant el desenvolupament d'una eina que pugui gestionar tots els vols a Europa de manera eficient. Per fer-ho, s’han explorat diferents enfocaments de predicció de trajectòries basats en models d’aprenentatge automàtic entrenats amb dades històriques, avaluant l’exercici de la predicció. A l’hora d’aplicar les tècniques d’aprenentatge automàtic per a la predicció de trajectòries, s’han identificat tres eleccions metodològiques fonamentals: (i) el clustering de trajectòries, que s’utilitza per agrupar trajectòries similars per simplificar el problema de predicció de trajectòries; (ii) la formulació del model d’aprenentatge automàtic; i (iii) l’aproximació seguida per entrenar el model. La contribució d’aquesta tesi doctoral a l’estat de l’art es troba a les dues primeres àrees. Primer, hem desenvolupat una nova tècnica de clustering de rutes, basada en l’àrea compresa entre dues rutes, que redueix el temps computacional requerit i augmenta l’escalabilitat respecte a altres tècniques de clustering descrites a la literatura. En segon lloc, hem desenvolupat, provat i avaluat dos nous enfocaments de modelatge per a la predicció de rutes. El primer enfocament consisteix a construir i entrenar un model d’aprenentatge automàtic independent per a cada parell de d'aeroports a la xarxa, prenent com a entrades diferents variables disponibles de les intencions més altres variables relacionades amb el clima i el nombre de regulacions. Aquest enfocament millora el rendiment del model PREDICT, però també té una limitació important: no considera canvis en l’estructura de l’espai aeri, per la qual cosa no podeu predir rutes que no estan disponibles a les dades d’entrenament i, de vegades, podeu predir rutes que no són compatibles amb l’estructura de l’espai aeri. El segon enfocament, basat en les aerolínies, consisteix a construir i entrenar un model independent per a cada aerolínia. Les limitacions del primer model se superen en considerar com a variables d’entrada no només les variables disponibles dels intencions i el clima, sinó també les restriccions de l’espai aeri i les característiques de la ruta (p. ex., cost de la ruta, longitud, etc.). L’enfocament basat en aerolínies produeix una millora significativa respecte a PREDICT i al model basat en parells d'aeroports, aconseguint una precisió de predicció de ruta del 0,896 (comparant amb la precisió de PREDICT del 0,828), alhora que el problema pot escalar a tota l'àrea al complet amb un temps de computació raonable.(Español) El objetivo de la gestión de demanda y capacidad de tráfico (ATFCM por sus siglas en inglés) es garantizar que la capacidad aeroportuaria y del espacio aéreo satisfagan la demanda de tráfico mientras se optimizan los flujos para evitar exceder la capacidad disponible cuando esta no se puede aumentar más. En Europa, el ATFCM está a cargo de EUROCONTROL y consta de tres fases: estratégica, pre-táctica y táctica. Esta tesis se centra en la pre-táctica, que abarca los seis días previos al día de operaciones. Durante la fase pre-táctica, los usuarios del espacio aéreo han presentado pocos o ningún plan de vuelo y la única información sobre los vuelos disponible para EUROCONTROL son las llamados Intenciones de vuelo, que consisten principalmente en los horarios. La trayectoria está disponible sólo cuando los usuarios envían sus planes. Esta información es necesaria para asegurar una correcta asignación de recursos en coordinación con los provedores de servicios de navegación aérea de los distintos estados. Para predecir los FPLs antes de que sean presentados, EUROCONTROL se apoya en la herramienta PREDICT, que genera predicciones de tráfico de acuerdo las trayectorias elegidas por vuelos similares en el pasado reciente, sin aprovechar la información sobre las decisiones en datos históricos. El objetivo de la presente tesis doctoral es mejorar el desempeño predictivo de la herramienta PREDICT mediante el desarrollo de una herramienta que pueda gestionar todos los vuelos en Europa de una forma eficiente. Para ello, se han explorado diferentes enfoques de predicción de trayectorias basados en modelos de aprendizaje automático. A la hora de aplicar las técnicas de aprendizaje automático para predicción de trayectorias, se han identificado tres elecciones metodológicas fundamentales: (i) el clustering de trayectorias, que se utiliza para agrupar trayectorias similares a fin de simplificar el problema de predicción de trayectorias; (ii) la formulación del modelo de aprendizaje automático; y (iii) la aproximación seguida para entrenar el modelo. La contribución de esta tesis doctoral al estado del arte se encuentra en las dos primeras áreas. Primero, hemos desarrollado una novedosa técnica de clustering de rutas, basada en el área comprendida entre dos rutas, que reduce el tiempo computacional requerido y aumenta la escalabilidad con respecto a otras técnicas de clustering en la literatura. En segundo lugar, hemos desarrollado, probado y evaluado dos nuevos enfoques de modelado para la predicción de rutas. El primer enfoque consiste en construir y entrenar un modelo de aprendizaje automático independiente para cada par de aeropuertos en la red, tomando como entradas diferentes variables disponibles de las intenciones de vuelo más otras variables relacionadas con la meteorología y el número de regulaciones. Este enfoque mejora el rendimiento del modelo PREDICT, pero también tiene una limitación importante: no considera cambios en la estructura del espacio aéreo, por lo que no xvii puede predecir rutas que no están disponibles en los datos de entrenamiento y, a veces, puede predecir rutas que no son compatibles con el estructura del espacio aéreo. El segundo enfoque, basado en las aerolíneas, consiste en construir y entrenar un modelo independiente para cada aerolínea. Las limitaciones del primer modelo se superan al considerar como variables de entrada no solo las variables disponibles de las FIs y la meteorología, sino también las restricciones del espacio aéreo y las características de la ruta (p. ej., coste de la ruta, longitud, etc.). El enfoque basado en aerolíneas produce una mejora significativa con respecto a PREDICT y al modelo basado en pares de aeropuertos, logrando una precisión de predicción de ruta de 0,896 (frente a la precisión de PREDICT de 0,828), a la vez que puede lidiar con toda la red en un tiempo de computación razonable. Estos prometedores resultados nos animan a ser optimistas sobre una futura implementación del sistema propuesto.Ciència i tecnologies aeroespacial

Airspace analysis for greener operations: towards more adoptability and predictability of continuous descent approach (cda)

Continuous Descent Approach (CDA), also known as Optimized Profile Descent (OPD), is the advanced flight technique for commercial aircraft to descend continuously from cruise altitude to Final Approach Fix (FAF) or touchdown without level-offs and with- or near-idle thrust setting. Descending using CDA, aircraft stays as high as possible for longer time thereby expanding the vertical distance between aircraft\u27s sources of noise and ground, and thus significantly reducing the noise levels for populated areas around airports. Also, descending with idle engines, fuel burn is reduced resulting in reduction of harmful emissions to the environment and fuel consumption to air carriers. Due to safety considerations, CDA procedures may require more separation between aircraft, which could reduce the full utilization of runway capacity. Thus, CDA has been limited to low to moderate traffic levels at airports. Several studies in literature have used various approaches to present solutions to the problem of increasing the CDA implementation during periods of high traffic at airports. However, insufficient attention was given to define thresholds that would help Air Traffic Controllers (ATC) to manage and accommodate more CDA operations, strategically and tactically. Bridging this gap is the main intent of this work. This research focus is on increasing CDA operations at airports during high traffic levels by considering factors that impact its CDA adoption as they relate to airports\u27 demographics, and airspace around them {known as terminal maneuvering area (TMA)}. To capture the effect of these factors on CDA Adoptability (CDA-A), in general, and CDA Predictability (CDA-P), at the operational level, two (2) approaches are introduced. The CDA-A model defines and captures the maximum level of traffic threshold for CDA adoption. The model captures the factors affecting CDA in a single measure, which are designated collectively as the Probability of Blocking. It is defined as the fraction of time an aircraft\u27s request to embark on CDA is denied. The denial could emanate from safety concerns as well as other operational conditions, such as the congestion of the stacking space within the TMA. This metric should enhance ATC on the strategic level to increasing CDA operations during possibly higher traffic than normally the case. The other approach is for a CDA-P. This model is developed based on data-driven system approach. It extracts traffic features, such as aircraft type and speed, altitude, and rate of descent; from actual flights data to aid in further operational utilization of CDA in real time. By accurately predicting CDA instances during high traffic at airports, the CDA-P model should assist ATC manage adopting more CDA operations during periods of high demand. Through its framework, the CDA-P model utilizes Feature Engineering and Hierarchal Clustering Analysis, to facilitate descent profile visualization and labeling, for building, training, testing, and validation of CDA predictive models using Decision Trees with AdaBoost and Support Vector Machines (SVM). The CDA-P model is validated using actual flight data operated at Nashville Int\u27l Airport (BNA)

Trajectory Clustering for Air Traffic Categorisation

Availability of different types of data and advances in data-driven techniques open the path to more detailed analyses of various phenomena. Here, we examine the insights that can be gained through the analysis of historical flight trajectories, using data mining techniques. The goal is to learn about usual (or nominal) choices airlines make in terms of routing, and their relation with aircraft types and operational flight costs. The clustering is applied to intra-European trajectories during one entire summer season, and a statistical test of independence is used to evaluate the relations between the variables of interest. Even though about half of all flights are less than 1000 km long, and mostly operated by one airline, along one trajectory, the analysis shows that, for longer flights, there exists a clear relation between the trajectory clusters and the operating airlines (in about 49% of city pairs) and/or the aircraft types (30%), and/or the flight costs (45%)

Analysis of Airspace Traffic Structure and Air Traffic Control Techniques

Air traffic controller cognitive processes are a limiting factor in providing safe and efficient flow of traffic. Therefore, there has been work in understanding the factors that drive controllers decision-making processes. Prior work has identified that the airspace structure, defined by the reference elements, procedural elements and pattern elements of the traffic, is important for abstraction and management of the traffic. This work explores in more detail this relationship between airspace structure and air traffic controller management techniques. This work looks at the current National Airspace System (NAS) and identifies different types of high altitude sectors, based on metrics that are likely to correlate with tasks that controllers have to perform. Variations of structural patterns, such as flows and critical points were also observed. These patterns were then related to groupings by origins and destinations of the traffic. Deeper pilot-controller voice communication analysis indicated that groupings by flight plan received consistent and repeatable sequences of commands, which were identified as techniques. These repeated modifications generated patterns in the traffic, which were naturally associated with the standard flight plan groupings and their techniques. The identified relationship between flight plan groupings and management techniques helps to validate the grouping structure-base abstraction introduced by Histon and Hansman (2008). This motivates the adoption of a grouping-focused analysis of traffic structures on the investigation of how new technologies, procedures and concepts of operations will impact the way controllers manage the traffic. Consideration of such mutual effects between structure and controllers' cognitive processes should provide a better foundation for training and for engineering decisions that include a human-centered perspective.This work was financially supported by FAA grant 06-G-006 and NASA Cooperative Agreement NN06CN23A. Anton Koros and Eddie Sierra were the technical sponsors and provided valuable feedback and assistance

A reduced order modeling methodology for the parametric estimation and optimization of aviation noise

The successful mitigation of aviation noise is one of the key enablers of sustainable aviation growth. Technological improvements for noise reduction at the source have been countered by increasing number of operations at most airports. There are several consequences of aviation noise including direct health effects, effects on human and non-human environments, and economic costs. Several mitigation strategies exist including reduction of noise at source, land-use planning and management, noise abatement operational procedures, and operating restrictions. Most noise management programs at airports use a combination of such mitigation measures. To assess the efficacy of noise mitigation measures, a robust modeling and simulation capability is required. Due to the large number of factors which can influence aviation noise metrics, current state-of-the-art tools rely on physics-based and semi-empirical models. These models help in accurately predicting noise metrics in a wide range of scenarios; however, they are computationally expensive to evaluate. Therefore, current noise mitigation studies are limited to singular applications such as annual average day noise quantification. Many-query applications such as parametric trade-off analyses and optimization remain elusive with the current generation of tools and methods. There are several efforts documented in literature which attempt to speed up the process using surrogate models. Techniques include the use of pre-computed noise grids with calibration models for non-standard conditions. These techniques are typically predicated on simplifying assumptions which greatly limit the applicability of such models. Simplifying assumptions are needed to downsize the number influencing factors to be modeled and make the problem tractable. Existing efforts also suffer due to the inclusion of categorical variables for operational profiles which are not conducive to surrogate modeling. In this research, a methodology is developed to address the inherent complexities of the noise quantification process, and thus enable rapid noise modeling capabilities which can facilitate parametric trade-off analysis and optimization efforts. To achieve this objective, a research plan is developed and executed to address two major gaps in literature. First, a parametric representation of operational profiles is proposed to replace existing categorical descriptions. A technique is developed to allow real-world flight data to be efficiently mapped onto this parametric definition. A trajectory clustering method is used to group similar flights and representative flights are parametrized using an inverse-map of an aircraft performance model. Next, a field surrogate modeling method is developed based on Model Order Reduction techniques to reduce the high dimensionality of computed noise metric results. This greatly reduces the complexity of data to be modeled, and thus enables rapid noise quantification. With these two gaps addressed, the overall methodology is developed for rapid noise quantification and optimization. This methodology is demonstrated on a case study where a large number of real-world flight trajectories are efficiently modeled for their noise results. As each such flight trajectory has a unique representation, and typically lacks thrust information, such noise modeling is not computationally feasible with existing methods and tools. The developed parametric representations and field surrogate modeling capabilities enable such an application.Ph.D

RETROSPECTIVE AND EXPLORATORY ANALYSES FOR ENHANCING THE SAFETY OF ROTORCRAFT OPERATIONS

From recent safety reports, the accident rates associated with helicopter operations have reached a plateau and even have an increasing trend. More attention needs to be directed to this domain, and it was suggested to expand the use of flight data recorders on board for monitoring the operation. With the expected growth of flight data records in the coming years, it is essential to conduct analyses and provide the findings to the operator for risk mitigation. In this thesis, a retrospective analysis is proposed to detect potential anomalies in the fight data for rotorcraft operations. In the study, an algorithm is developed to detect the phases of flight for segmenting the flights into homogeneous entities. The anomaly detection is then performed on the flight segments within the same flight phases, and it is implemented through a sequential approach. Aside from the retrospective analysis, the exploratory analysis aims to efficiently find the safety envelope and predict the recovery actions for a hazardous event. To facilitate the exploration of the corresponding operational space, we provide a framework consisting of surrogate modeling and the design of experiments for tackling the tasks. In the study, the autorotation, a maneuver used to land the vehicle under power loss, is treated as a used case to test and validate the proposed framework.Ph.D

Analyse et détection des trajectoires d'approches atypiques des aéronefs à l'aide de l'analyse de données fonctionnelles et de l'apprentissage automatique

L'amélioration de la sécurité aérienne implique généralement l'identification, la détection et la gestion des événements indésirables qui peuvent conduire à des événements finaux mortels. De précédentes études menées par la DSAC, l'autorité de surveillance française, ont permis d'identifier les approches non-conformes présentant des déviations par rapport aux procédures standards comme des événements indésirables. Cette thèse vise à explorer les techniques de l'analyse de données fonctionnelles et d'apprentissage automatique afin de fournir des algorithmes permettant la détection et l'analyse de trajectoires atypiques en approche à partir de données sol. Quatre axes de recherche sont abordés. Le premier axe vise à développer un algorithme d'analyse post-opérationnel basé sur des techniques d'analyse de données fonctionnelles et d'apprentissage non-supervisé pour la détection de comportements atypiques en approche. Le modèle sera confronté à l'analyse des bureaux de sécurité des vols des compagnies aériennes, et sera appliqué dans le contexte particulier de la période COVID-19 pour illustrer son utilisation potentielle alors que le système global ATM est confronté à une crise. Le deuxième axe de recherche s'intéresse plus particulièrement à la génération et à l'extraction d'informations à partir de données radar à l'aide de nouvelles techniques telles que l'apprentissage automatique. Ces méthodologies permettent d'améliorer la compréhension et l'analyse des trajectoires, par exemple dans le cas de l'estimation des paramètres embarqués à partir des paramètres radar. Le troisième axe, propose de nouvelles techniques de manipulation et de génération de données en utilisant le cadre de l'analyse de données fonctionnelles. Enfin, le quatrième axe se concentre sur l'extension en temps réel de l'algorithme post-opérationnel grâce à l'utilisation de techniques de contrôle optimal, donnant des pistes vers de nouveaux systèmes d'alerte permettant une meilleure conscience de la situation.Improving aviation safety generally involves identifying, detecting and managing undesirable events that can lead to final events with fatalities. Previous studies conducted by the French National Supervisory Authority have led to the identification of non-compliant approaches presenting deviation from standard procedures as undesirable events. This thesis aims to explore functional data analysis and machine learning techniques in order to provide algorithms for the detection and analysis of atypical trajectories in approach from ground side. Four research directions are being investigated. The first axis aims to develop a post-op analysis algorithm based on functional data analysis techniques and unsupervised learning for the detection of atypical behaviours in approach. The model is confronted with the analysis of airline flight safety offices, and is applied in the particular context of the COVID-19 crisis to illustrate its potential use while the global ATM system is facing a standstill. The second axis of research addresses the generation and extraction of information from radar data using new techniques such as Machine Learning. These methodologies allow to \mbox{improve} the understanding and the analysis of trajectories, for example in the case of the estimation of on-board parameters from radar parameters. The third axis proposes novel data manipulation and generation techniques using the functional data analysis framework. Finally, the fourth axis focuses on extending the post-operational algorithm into real time with the use of optimal control techniques, giving directions to new situation awareness alerting systems