aircraft ground handling

1 March 2022

The increasing data recording at airports enables an ever better analysis of past handling processes on the airport apron. However, prediction of future procedures is only possible in conjunction with knowledge of process dependencies. The goal of this work is to design and prototype a prediction model for aircraft handling that implements a hybrid approach of recorded data (machine learning) and model-based support (physics-informed).

Aktuelles

the team is growing

Since January 2nd, Mr. Ehsan Asadi is a new member and team leader at the chair.

Synthetic sensors for automated airport operations

A prototype of a synthetic LiDAR sensor in a virtual environment of Singapore Changi Airport provides data to train machine learning models.

the team is growing

Since November 1st, Mr. Simon Göppel is a new member of the chair.

COVID-19 passenger operations

The implementation of optimized passenger group sequencing under COVID-19 conditions has the potential to reduce process times by up to 59% and transmission risk by up to 85%.

the team is growing

Since September 1, Mr. Falk Otto is a new member of the chair.

the team is growing

Since August 1, Mr. Fattahi Khaki is a new member of the chair.

integrated airport management

Integrated ground/airside airport management optimizes connecting flights for passengers.