Demographic change is prevalent in rural regions against the backdrop of an aging society as well as out-migration due to a lack of employment opportunities employment opportunities and poor infrastructure. Given a fixed budget for transport operators, declining demand leads to high operating costs per transported customer. This makes the provision of public transport economically inefficient and People in rural areas become highly dependent on private motorized transport. Accordingly, public transport needs to be transformed through digitalization measures. For this purpose, the DRT system Ecobus was developed for rural areas as a door-to-door system.

A major driver of climate change is flying, so we are striving to make this more sustainable. in doing so, we are focusing on researching the effects of operating conditions on aircraft engines to improve the timely planning of maintenance events and optimize them. To do this, we use machine learning mainly with time series of aircraft engines and numerical weather prediction models. The research aims to improve fuel efficiency and increase component longevity. This should enable plant operators and service providers to reduce costs and environmental footprint.

Status: Pre-Studies