This project aims at producing a fully open aircraft performance model. The OpenAP is about to model aircraft performance parameters involving kinematic, thrust, drag, and fuel flow. The repository contains all OpenAP databases and a Python implementation which facilitates data access and aircraft performance computation.

PyModeS is a Python library for decoding and encoding Mode S (including ADS-B) messages. It is an open-source project that receives great support and contributions from the aviation community. this python package can be imported into existing python project, and it also be used as a standalone tool to view and save live traffic data.

A Python library for wind field estimation based on the Meteo-Particle model. The wind and temperature are obtained from ADS-B and Mode S data using pyModeS.

Developing a guide for decoding ADS-B and Mode-S messages. Started as the journal of developing the pyModeS library. It has been growing into a project of writing an open-access book.

BlueSky is the open-source Air Traffic Simulation project started by prof. Hoekstra. I am contributing to the modules related to aircraft performance, data feed, wind models, etc.

Constructing a database to search for aircraft IDs (such as ICAO address, and registrations ID) and related information. It is built in Python / Flask and with data from Flight Radar 24.

A python library to process and analyze ADS-B flight data. It can reconstruct flight trajectories from ADS-B data and identify flight phases.

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

Student: R. Grass

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

Student: J.L. Overkamp

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

Student: J. Lubberding

This study aims at developing a method to verify and validate ADS-B signal integrity using low-cost concurrent multi-channel software defined radio receiver.

Student: W. Huygen

This study aims 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.

Student: J. Jongbloed

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

Student: A. Bonifazi

This study makes use of open ADS-B and Mode S to construct accurate wind and temperature field, which can be used to improved existing aircraft kinematic performance models.

Student: Q.H. Vû

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

Student: T.W. Gloudemans

This research use MLAT data to assist the analysis of ADS-B data and signal quality and to investigate the effects of internal and external factors on ADS-B performance implementation.

Student: T.L. Verbraak

Engage is managed by a consortium of academia and industry, with the support of the SESAR JU, to promote and facilitate the development of air traffic management research in Europe. The project forces on inspiring new researchers and helping to align exploratory and industrial research, through a wide range of activities and financial support actions.