In June 2020, we kicked off Dispatcher3, an Innovative Action within the frame of CleanSky 2, led by the University of Westminster (UoW) in collaboration with Innaxis, the Universitat Politècnica de Catalunya (UPC), Vueling Airlines, PACE Aerospace Engineering & Information Technology and the ANSP skeyes. This 2.5 year long project aims to improve dispatching processes by providing an infrastructure that is capable of leveraging historical data and machine learning techniques to systematically estimate variability between planned and executed flight plans.
These differences in flight plans are driven by a number of uncertainty factors, e.g. the weather or the delay due to holding upon arrival. In the current concept of operations, strategically, an airline considers its business objectives in order to identify the key performance areas (KPAs) that are of its interest and defines how they will be monitored with individual key performance indicators (KPIs). Such information is then used by schedule planners, dispatchers and pilots during the operations or to define specific flight policies.
This is where machine learning jumps in: by building intelligent estimators that utilise historical data to generate more accurate forecasts of various indicators, thus reducing uncertainty, machine learning can provide predictive capabilities that dispatchers, crew, network planners and other interested users can rely on to make more informed decisions when selecting flight plans or building schedule networks. To achieve that goal, Dispatcher3 relies on a cloud-based infrastructure powered by DataBeacon to store and process data. It aims to develop two modules:
Various user types can benefit from the software prototype developed in Dispatcher3:
Dispatcher3 was presented during the 2020 edition of SESAR Innovation Days (SIDs). The project teaser video is available at the following link and if you want to hear more details about the project methodology and other possible use cases, grab a drink, sit back and enjoy this five minute long video presentation originally shown at SIDs 2020.