Air Traffic Management hotspots in Europe with airline cost functions

This dataset contains data related to Air Traffic Management hotspots. Hotspots are created in the European airspaces when capacity for some pieces of airspace are foreseen to be infringed due to weather, congestion, strikes, etc. This anonymised dataset records around 5900 hotspots happening at 22 major European airports. These hotspots are generated through a simulator called Mercury that is fed with real data (in particular, real capacity reduction that happened in Europe for over a year, schedules etc) and simulates a day of operation, randomising events like delays, cancellation etc. More details on mercury can be found here [1] and [2].

The data, anonymised in terms of airports and airlines, is a dictionary which is structured as follows:

- the top level key is the id of the airport, the value is list a of all regulations available for this airport.

- each item of the list is a dictionary, with keys:

 -- 'slot_times': list of all slots available to flights for this hotspot/regulation, in minutes since midnight.

 -- 'etas': list of initial estimated arrival times of flights involved in the regulation, in minutes since midnight.

 -- 'flight_ids': list of flight ids (in the same order than etas)

 -- 'cost_vectors': list of cost vectors. Each item is a list itself, of length equal to the slot_times list. Each element of that list is the estimated cost that the airline owning the flight would incur, were the flight be assigned to this slot, in terms of: maintenance, crew, rebooking fees, market value loss, and curfew infringement, in 2014 euros. This cost is computed within the Mercury model and is based on [3].

 -- 'airlines_flights': dictionary whose keys are airline ids and values are lists of ids of flights owned by the airline.

[1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600 

[2] G. Gurtner, L. Delgado, and D.Valput, “An agent-based model for air transportation to capture network effects in assessing delay management mechanisms”, Transportation Research Part C: emerging Technologies, 2021.

Pre-print available here: https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms

[3] A. J. Cook and G. Tanner, “European airline delay cost reference values - updated and extended values (Version 4.1),” University of Westminster, London, 2015a