The research aims to quantify the environmental inefficiencies in flight plans and actual flight trajectories.

This research aims to incorporate large language models into post-processing of automatic speech recognition.

The research aims to use new machine learning models to generate 3D wind field from partially observable aircraft surveillance data.

The research explore methodologies for fairly managing and off-loading the capacities among sectors from the network manager perspective.

This research aims to develop a flight route optimization strategy to avoid climate-sensitive regions.

The research investigates the effect of hydrogen combustion technologies in aviation on trajectory optimisation strategies to reduce contrail climate impact.

This research explores the possibility of adopting large language models for air traffic control through embodied agents.

This research uses supervised machine learning methods to predict en-route delay before departure from the airline perspective.

This thesis investigates the feasibility of shifting passengers between air and rail on selected short-haul routes in Europe, considering rail network capacities, travel time, cost, emissions, and passenger experiences.

This research explores machine learning models to predicting the take-off weight based on flight plans and operation data.

This study designs algorithms that performs multilateration using received signal strength. OpenSky data is used to evaluate the feasibility and accuracy of the proposed algorithms.

The study assess the potential of causal analysis methods in uncovering the most significant inefficiencies in ATM operations.

This study explores fine-tuning the Whisper model for air traffic control, achieving significant error rate reductions, suggesting its potential for real-time application.

The research supports airline flight planning by developing machine learning models to forecast the departure delays, providing uncertainty and explainability.

This research enhances air traffic sector demand forecasting, by employing a transformer neural network to generate accurate aircraft trajectories, improving stability in demand predictions.

This thesis develops a tail-specific performance modeling framework using flight data and ML methods to optimize flight operations for fuel efficiency. It corrects biases in flight data, enabling post-flight analysis of fuel savings.

Designing models to assess emissions and environmental impact of aircraft and ground transport modes.

This study aims at constructing an open RT corpus and an ATCo speech recognition model using neural networks and domain-specific information.

Studying the inter-modal efficiency and analyze emission impact of layover flights in Europe using open data.

This research explores methodologies that incorporate dynamics of winds and improve the existing Meteo-Particle model under complex wind conditions.

This research explores the estimation of wind for drones using low-cost airspeed sensors and existing onboard sensors.

This research explores new ways to generate the most environmentally efficient trajectories. We also explore the environmental inefficiency in current standard arrival and departure practices and propose more efficient operational designs.

This study looks into how an ADS-B compatible system for UAVs can be designed and implemented.

In this research, we study how different surveillance signals for an aircraft can be processed and combined with machine learning approaches for the aircraft identification validation in CNS/ATM.

As part of the open aircraft performance (OpenAP) framework, in this research, we explored different optimization approaches to minimize the environmental impact of flight trajectories, with a focus for cruise flights.

This study investigated different methods to tackle the unpredictability of flight trajectories caused by various random factors. The goal was to model trajectory prediction uncertainty caused by these factors during different flight phases.

This study focused on the medium to long-term predictions of flight trajectories based on data-driven approaches. We aimed to model the dynamics of air traffic situations and incorporate this model to improve flight trajectory predictions.

This study developed a method to verify and validate ADS-B signal integrity with low-cost concurrent multi-channel software-defined radio receivers.

This study aimed at expanding OpenAP’s capability by developing an emission model, which can calculate emissions such as CO, NOx, CO2, and H2O based on ADS-B data.

The study proposed a proactive risk management strategy that uses safety indicators, which are obtained from ADS-B data using various data mining techniques. These safety indicators were then used to identify anomalous safety events and precursors at Schiphol Airport.

This study made use of open ADS-B and Mode S to construct accurate wind and temperature fields, and in turn, improved aircraft kinematic performance models.

This study made the first attempt to model flight envelope and estimate aircraft performance parameters using ADS-B data.

This research used MLAT data to assist the analysis of ADS-B data quality and to investigate the effects of internal and external factors affecting ADS-B implementation.