This research looks into data-driven approaches, making use of large-scale air traffic data and remote sensing data, to propose innovative solutions to accurately assess and reduce air transport’s environmental footprint in terms of emissions and contrails.PhD Candidate: E.J. Roosenbrand
This PhD research looks into different optimization approaches to mitigating the environmental impacts of flights, with focus in at arrival and departure flights.PhD Candidate: A. Tassanbi
This study tries to design algorithms that better utilize signal strength for MLAT.
Collaboration between TU Delft and NLR, research on how whisper model can improve ATC operations and safety.
The research supports airline flight planning by developing supervised learning models to forecast the departure delays.
The study uses aircraft surveillance data to assess environmental impact related to do air traffic operations.
This study focused on the medium to long-term predictions of flight trajectories based on neural networks.
The objective is to create a data-driven model that provides new cost index models for each individual aircraft, so that flights can be operated at the most economically.
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.