Prediction of Gate In Time of Scheduled Flights and Schedule Conformance using Machine Learning-based Algorithms

Prediction of Gate to Gate block time for scheduled flights is considered as one of the challenging tasks in Air Traffic Flow Management (ATFM)system. Establishing an effective and practically reliable model to manage the problem of block time variation is a significant work. The airlines do tend to pad or inflate block time to Actual Block time to calculate Schedule block times which is approved by aviation regulator. This will lead to flaws in air traffic flow strategic decision-making and in turn affect the efficiency, estimation and undesirable delays, which leads to traffic congestion and inefficient ground delay programs. This study evaluates the effectiveness of nonlinear and time varying regression models to predict block time with minimal attributes in order to solve the problem of difficulty in predicting the block time variation. The key research outcome of this paper is to trace the temporal variations of flying time for different aircraft types and to predict the variation of actual arrival time from the scheduled arrival time at the destination airport. Ultimately, a combination of M5P regression model and logistic regression model is proposed to predict early, delayed and on-time conformity with approved schedules. Analysis based on a realistic data set of a domestic airport pair (Mumbai International Airport and New Delhi International Airport) in India shows that the proposed model is able to predict in block time at the time of departure with an accuracy of minutes for of test instances. As a result of the scheduled arrival time performance (early, delayed and timely) has been classified accurately using Logistic regression Classifier of machine learning. The test results show that the proposed model uses a minimum number of attributes and less computational time to more accurately predict the actual arrival time and scheduled arrival performance without details on the weather

A Machine Learning Approach Towards Analyzing Impact of Surface Weather on Expect Departure Clearance Times in Aviation

Commercial air travel in the United States has grown significantly in the past decade. While the reasons for air traffic delays can vary, the weather is the largest cause of flight cancellations and delays in the United States. Air Traffic Control centers utilize Traffic Management Initiatives such as Ground Stops and Expect Departure Clearance Times (EDCT) to manage traffic into and out of affected airports. Airline dispatchers and pilots monitor EDCTs to adjust flight blocks and flight schedules to reduce the impact on the airline’s operating network. The use of time-series data mining can be used to assess and quantify the impact of surface weather variables on EDCTs. A major hub airport in the United States, Charlotte Douglas International Airport, was chosen for the model development and assessment, and Vector Autoregression and Recurrent Neural Network models were developed. While both models were assessed to have demonstrated acceptable performance for the assessment, the Vector Autoregression outperformed the Recurrent Neural Network model. Weather variables up to six hours before the prediction time period were used to develop the proposed lasso regularized Vector Autoregression equation. Precipitation values were assessed to be the most significant predictors for EDCT values by the Vector Autoregression and Recurrent Neural Network models

A deep BiLSTM machine learning method for flight delay prediction classification

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features to train and test the models. The performance evaluation of the models and Confusion matrix shows that BiLSTM outperforms the LSTM model. In evaluating the models using the Mathews Correlation Coefficient (MCC), the BiLSTM model offers a better correlation of 0.99 between the original and predicted classes. Our experiment shows that for predicting flight delays, the BiLSTM model takes advantage of the forward and backward hidden sequences and the deep neural network for performance exploration and exploitation to achieve high accuracy, recall, and F1-Score. Our findings suggest that the BiLSTM model can effectively predict flight delays and provide valuable information for airlines, passengers, and airport managers

Predictability improvement of Scheduled Flights Departure Time Variation using Supervised Machine Learning

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air traffic flow management slot changes. This paper presents a model that predicts departure time variance based on the previous leg departure time using a combination of exponential moving average and machine learning methods. The model correctly classifies the departure time (Early, On Time, Delay) based on the previous leg departure state, allowing the ATFM system to measure the arrival time of a capacity constrained airport with greater accuracy and better assess demand requirements. The results show that the proposed model with M5P Regression tree provides the best results, with Mean Absolute Error and Root Mean Square Error (RMSE) of 3.43 and 4.83, respectively, indicating a 50% improvement over previous research findings. Whereas, with logistic regression, the classification of departure time (Early, On Time, Delay) is achieved a better accuracy of 91 %, which is higher than previous works

An Innovative Human Machine Interface for UAS Flight Management System

The thesis is relative to the development of an innovative Human Machine Interface for UAS Flight Management System. In particular, touchscreena have been selected as data entry interface. The thesis has been done together at Alenia Aermacch

Disrupting aviation: an exploratory study of the opportunities and risks of tablet computers in commercial flight operations

Commercial flight operational safety has dramatically improved in the last 30 years because of enhanced crew coordination, communication, leadership and team development. Technology insertion into cockpit operations, however, has been shown to create crew distractions, resulting in flight safety risks, limited use given policy limitations and difficulty in establishing standard operating procedures. With the recent introduction of tablet computers into the flight cockpit as a substitute for paper-based manuals and navigation charts, the risk of human error may be increased. Though portable electronics, known as electronic flight bags, have been present of the flight deck for a decade, introduction of tablet computers as their replacements offers unique challenges, given the ability to communicate and share information outside established aviation channels. This research explored the opportunities that this technology insertion offers to commercial aviation in areas such as knowledge sharing and operational performance improvement. The results indicate that the opportunities were not realized with the initial implementation because the pilots did not accept the technology due to inadequate training coupled with restrictive policies concerning use

Contributions to the Optimisation of aircraft noise abatement procedures

Tot i que en les últimes dècades la reducció del soroll emès pels avions ha estat substancial, el seu impacte a la població ubicada a prop dels aeroports és un problema que encara persisteix. Contenir el soroll generat per les operacions d'aeronaus, tot assumint al mateix temps la creixent demanda de vols, és un dels principals desafiaments a que s'enfronten les autoritats aeroportuàries, els proveïdors de serveis per a la navegació aèria i els operadors de les aeronaus. A part de millorar l'aerodinàmica o les emissions sonores de les aeronaus, l'impacte acústic de les seves operacions es pot reduir també gràcies a la definició de nous procediments de vol més òptims. Aquests procediments s'anomenen generalment Procediments d'Atenuació de Soroll (PAS) i poden incloure rutes preferencials de vol (a fi d'evitar les zones poblades) i també perfils de vol verticals optimitzats. Els procediments actuals per a la reducció de soroll estan molt lluny de ser els òptims. En general, la seva optimització no és possible a causa de les limitacions d'avui en dia en els mètodes de navegació, els equips d'aviònica i la complexitat present en alguns espais aeris. D'altra banda, molts PAS s'han dissenyat de forma manual per un grup d'experts i amb l'ajuda de diverses iteracions. Tot i això, en els propers anys s'esperen nous sistemes d'aviònica i conceptes de gestió del trànsit aeri que permetin millorar el disseny d'aquests procediments, fent que siguin més flexibles. En els pocs casos on s'optimitzen PAS, se sol utilitzar una mètrica acústica en l'elaboració de les diferents funcions objectiu i per tant, no es tenen en compte les molèsties sonores reals. La molèstia és un concepte subjectiu, complexe i que depèn del context en que s'usa i la seva integració en l'optimització de trajectòries segueix essent un aspecte a estudiar.La present tesi doctoral es basa en el fet que en el futur serà possible definir trajectòries més flexibles i precises. D'aquesta manera es permetrà la definició de procediments de vol òptims des d'un punt de vista de molèsties acústiques. Així doncs, es considera una situació en que aquest tipus de procediments poden ser dissenyats de forma automàtica o semi-automàtica per un sistema expert basat en tècniques d'optimització i de raonament aproximat. Això serviria com una eina de presa de decisions per planificadors de l'espai aeri i dissenyadors de procediments. En aquest treball es desenvolupa una eina completa pel càlcul de PAS òptims. Això inclou un conjunt de models no lineals que tinguin en compte la dinàmica de les aeronaus, les limitacions de la trajectòria i les funcions objectiu. La molèstia del soroll es modela utilitzant tècniques de lògica difusa en funció del nivell màxim de so percebut, l'hora del dia i el tipus de zona a sobrevolar. Llavors, s'identifica i es formula formalment el problema com a un problema de control òptim multi-criteri. Per resoldre'l es proposa un mètode de transcripció directa per tal de transformar-lo en un problema de programació no lineal. A continuació s'avaluen una sèrie de tècniques d'optimització multi-objectiu i entre elles es destaca el mètode d'escalarització, el més utilitzat en la literatura. No obstant això, s'exploren diverses tècniques alternatives que permeten superar certs inconvenients que l'escalarització presenta. En aquest context, es presenten i proven tècniques d'optimització lexicogràfica, jeràrquica, igualitària (o min-max) i per objectius. D'aquest anàlisi es desprenen certes conclusions que permeten aprofitar les millors característiques de cada tècnica i formar finalment una tècnica composta d'optimització multi-objectiu. Aquesta última estratègia s'aplica amb èxit a un escenari real i complex, on s'optimitzen les sortides cap a l'Est de la pista 02 de l'aeroport de Girona. En aquest exemple, dos tipus diferents d'aeronaus volant a diferents períodes del dia són simulats obtenint, conseqüentment, diferents trajectòries òptimes.Aunque en las últimas décadas la reducción del ruido emitido por los aviones ha sido sustancial, su impacto en la población ubicada cerca de los aeropuertos es un problema persistente. Contener este ruido, asumiendo al mismo tiempo la creciente demanda de vuelos, es uno de los principales desafíos a que se enfrentan las autoridades aeroportuarias, los proveedores de servicios para la navegación y los operadores. Aparte de mejorar la aerodinámica o las emisiones sonoras de las aeronaves, su impacto acústico se puede reducir también gracias a la definición de nuevos procedimientos de vuelo optimizados. Éstos, se denominan generalmente Procedimientos de Atenuación de Ruido (PAR) y pueden incluir rutas preferenciales de vuelo (a fin de evitar las zonas pobladas) y también perfiles de vuelo optimizados.Los procedimientos actuales para la reducción de ruido están muy lejos de ser los óptimos. En general, su optimización no es posible debido a las limitaciones de hoy en día en los métodos de navegación, los equipos de aviónica y la complejidad presente en algunos espacios aéreos. Por otra parte, muchos PAR se han diseñado de forma manual por un grupo de expertos y con la ayuda de varias iteraciones. Sin embargo, en los próximos años se esperan nuevos sistemas de aviónica y conceptos de gestión del tráfico aéreo que permitan mejorar el diseño de estos procedimientos, haciendo que sean más flexibles. En los pocos casos donde se optimizan PAR, se suele utilizar una métrica acústica en la elaboración de las diferentes funciones objetivo y por lo tanto, no se tienen en cuenta las molestias sonoras reales. La molestia es un concepto subjetivo, complejo y que depende del contexto en que se usa y su integración en la optimización de trayectorias sigue siendo un aspecto a estudiar. La presente tesis doctoral se basa en el hecho de que en el futuro será posible definir trayectorias más flexibles y precisas. De esta manera se permitirá la definición de procedimientos de vuelo óptimos desde un punto de vista de molestias acústicas. Se considera una situación en que este tipo de procedimientos pueden ser diseñados de forma automática o semi-automática por un sistema experto basado en técnicas de optimización y de razonamiento aproximado. Esto serviría como una herramienta de toma de decisiones para planificadores del espacio aéreo y diseñadores de procedimientos.En este trabajo se desarrolla una herramienta completa para el cálculo de PAR óptimos. Esto incluye un conjunto de modelos no lineales que tengan en cuenta la dinámica de las aeronaves, las limitaciones de la trayectoria y las funciones objetivo. La molestia del ruido se modela utilizando técnicas de lógica difusa en función del nivel máximo de sonido percibido, la hora del día y el tipo de zona a sobrevolar. Entonces, se identifica y se formula formalmente el problema como un problema de control óptimo multi-criterio. Para resolverlo se propone un método de transcripción directa para transformarlo en un problema de programación no lineal. A continuación se evalúan una serie de técnicas de optimización multi-objetivo y entre ellas se destaca el método de escalarización, el más utilizado en la literatura. Sin embargo, se exploran diversas técnicas alternativas que permiten superar ciertos inconvenientes que la escalarización presenta. En este contexto, se presentan y prueban técnicas de optimización lexicográfica, jerárquica, igualitaria (o min-max) y por objetivos. De este análisis se desprenden ciertas conclusiones que permiten aprovechar las mejores características de cada técnica y formar finalmente una técnica compuesta de optimización multi-objetivo. Esta última estrategia se aplica con éxito en un escenario real y complejo, donde se optimizan las salidas hacia el Este de la pista 02 del aeropuerto de Girona. En este ejemplo, dos tipos diferentes de aeronaves volando a diferentes periodos del día son simulados obteniendo, consecuentemente, diferentes trayectorias óptimas.Despite the substantial reduction of the emitted aircraft noise in the last decades, the noise impact on communities located near airports is a problem that still lingers. Containing the sound generated by aircraft operations, while meeting the increasing demand for aircraft transportation, is one of the major challenges that airport authorities, air traffic service providers and aircraft operators may deal with. Aircraft noise can be reduced by improving the aerodynamics of the aircraft, the engine noise emissions but also in designing new optimised flight procedures. These procedures, are generally called Noise Abatement Procedures (NAP) and may include preferential routings (in order to avoid populated areas) and also schedule optimised vertical flight path profiles. Present noise abatement procedures are far from being optimal in regards to minimising noise nuisances. In general, their optimisation is not possible due to the limitations of navigation methods, current avionic equipments and the complexity present at some terminal airspaces. Moreover, NAP are often designed manually by a group of experts and several iterations are needed. However, in the forthcoming years, new avionic systems and new Air Traffic Management concepts are expected to significantly improve the design of flight procedures. This will make them more flexible, and therefore will allow them to be more environmental friendly. Furthermore, in the few cases where NAP are optimised, an acoustical metric is usually used when building up the different optimisation functions. Therefore, the actual noise annoyance is not taken into account in the optimisation process. The annoyance is a subjective, complex and context-dependent concept. Even if sophisticated noise annoyance models are already available today, their integration into an trajectory optimisation framework is still something to be further explored. This dissertation is mainly focused on the fact that those precise and more flexible trajectories will enable the definition of optimal flight procedures regarding the noise annoyance impact, especially in the arrival and departure phases of flights. In addition, one can conceive a situation where these kinds of procedures can be designed automatically or semi-automatically by an expert system, based on optimisation techniques and approximate reasoning. This would serve as a decision making tool for airspace planners and procedure designers.A complete framework for computing optimal NAP is developed in this work. This includes a set of nonlinear models which take into account aircraft dynamics, trajectory constraints and objective functions. The noise annoyance is modelled by using fuzzy logic techniques in function of the perceived maximum sound level, the hour of the day and the type of over-flown zone. The problem tackled, formally identified and formulated as a multi-criteria optimal control problem, uses a direct transcription method to transform it into a Non Linear Programming problem. Then, an assessment of different multi-objective optimisation techniques is presented. Among these techniques, scalarisation methods are identified as the most widely used methodologies in the present day literature. Yet, in this dissertation several alternative techniques are explored in order to overcome some known drawbacks of this technique. In this context, lexicographic, hierarchical, egalitarian (or min-max) and goal optimisation strategies are presented and tested. From this analysis some conclusions arise allowing us to take advantage of the best features of each optimisation technique aimed at building a final compound multi-objective optimisation strategy. Finally, this strategy is applied successfully to a complex and real scenario, where the East departures of runway 02 at the airport of Girona (Catalonia, Spain) are optimised. Two aircraft types are simulated at different periods of the day obtaining different optimal trajectories.Postprint (published version

Aeronautical life-cycle mission modelling framework for conceptual design

This thesis introduces a novel framework for life cycle mission modelling during conceptual aeronautical design. The framework supports object-oriented mission definition using Geographical Information System technology. Design concepts are defined generically, enabling simulation of most aeronautical vessels and many non-aeronautical vehicles. Moreover, the framework enables modelling of entire vessel fleets, business competitors and dynamic operational changes throughout a vessel life cycle. Vessels consist of components deteriorating over time. Vessels carry payload that operates within the vessel environment.An agent-based simulation model implements most framework features. It is the first use of an agent-based simulation utilising a Geographical Information System during conceptual aeronautical design. Two case studies for unmanned aircraft design apply the simulation. The first case study explores how the simulation supports conceptual design phase decisions. It simulates four different unmanned aircraft concepts in a search-and-rescue scenario including lifeboats. The goal is to learn which design best improves life cycle search performance. It is shown how operational and geographical impacts influence design decision making by generating novel performance information. The second case study studies the simulation optimisation capability: an existing aircraft design is modified manually based on simulation outputs. First, increasing the fuel tank capacity has a negative effect on life cycle performance due to mission constraints. Therefore, mission definition becomes an optimisation parameter. Changing mission flight speeds during specific segments leads to an overall improved design

12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"

Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin

Using machine learning methods in airline flight data monitoring to generate new operational safety knowledge from existing data

The aim of this work is to investigate the possibility of using machine learning (ML) methods in order to generate novel, safety-relevant knowledge from existing flight data. Airlines routinely generate vast amounts of flight data from routine monitoring, but the concept of extracting safety knowledge from this data is still based on detecting exceedances of expert-defined thresholds. This system is conceptually unable to detect novel occurrences for which no such filters exist. ML techniques are able to close this gap. This paper first reviews the literature to select an appropriate ML method. A form of unsupervised learning called “Local Outlier Probability” is selected. Next, an appropriate feature space is developed and implemented in the flight data monitoring system of a supporting airline to generate the dataset. This dataset is cleaned and the outlier calculation performed. The results are statistically analysed. Furthermore, the top outliers are reviewed by the airline’s review pilots in the same way as the traditional exceedance events. Last, the severities and safety relevance of both types of events are compared. This work successfully shows that the chosen approach is able to reduce the number of undetected safety-relevant occurrences by finding novel occurrence types which were undetected by a contemporary and mature flight data monitoring system. This research builds on recent literature by developing a novel method which can be scaled to work in an airline production environment with large datasets, as demonstrated by the efficient analysis of 1.2 million flights