Data-driven Aviation Management

2019 07 18 Moll to Lead Munich Aerospace Research Group
Dr. Maximilian Moll heads the Munich Aerospace Research Group "Data-driven Aviation Management"

18 July 2019 – The better airlines and rail transport providers are able to inter-coordinate, the greater the benefit for the environment. Members of the Munich Aerospace research group "Data-driven Aviation Management" are studying how optimising coordination between air and rail transport can alleviate snarled traffic.

If passengers preferred taking the train over short-haul flights, emissions could be reduced, relieving the burden on airports in the process. Making gains in the area of air and rail transport intermodality could thus unlock tremendous potential, shaping the mobility of tomorrow. The Munich Aerospace research group "Data-driven Aviation Management" is to examine ways of achieving greater intermodality from three perspectives: the environment, transport capacity and travel planning. These are closely linked, for low-emission travel can only be planned within the existing rail and flight capacity limits.

Environmental studies can be done for individual means of transport, but the findings are only significant when viewed in the larger context. This requires linking transport capacity data to individual travel planning data. The research group is devising proposals for improving rail and air traffic intermodality within the next few years through precision data gathering at select European airports like Munich, Frankfurt and Amsterdam.

The Munich Aerospace research group "Data-driven Aviation Management" is a collaboration between University of the German Armed Forces (Institute of Operations Research), the Technical University of Munich (Institute of Aircraft Design) and Bauhaus Luftfahrt (Team Economics & Transportation), headed by Dr. Maximilian Moll since July 2019. Dr. Moll’s research interests include quantitative decision support methods (descriptive analytics in particular) focussing on combining reinforcement learning with classical operations research as well as quantum computing.

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