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本数据集涵盖空中交通管理（Air Traffic Management）热点相关数据。欧洲空域内的热点，指因天气、空域拥堵、罢工等因素，预期部分空域的运行容量将被超出时所形成的管控热点。本匿名数据集记录了欧洲22座主要机场发生的约5900个热点事件。这些热点通过一款名为Mercury的模拟器生成：该模拟器以真实数据（尤其是欧洲地区连续一年多的实际容量缩减情况、航班时刻表等）作为输入，模拟单日空域运行流程，并对延误、航班取消等事件进行随机化处理。有关Mercury的更多详细信息可参见文献[1]与[2]。 本数据集已对机场与航空公司信息进行匿名化处理，整体采用字典结构，具体组织形式如下： - 顶层键为机场的唯一标识，其对应的值为该机场所有可用管控规则的列表。 - 列表中的每个元素均为字典，包含以下键： -- 'slot_times'：该热点/管控规则对应的可供航班使用的所有时刻槽（slot）时段列表，单位为自午夜起的分钟数。 -- 'etas'：涉及该管控规则的航班的初始预计到达时间列表，单位为自午夜起的分钟数。 -- 'flight_ids'：航班标识列表（与'etas'的顺序保持一致）。 -- 'cost_vectors'：成本向量列表。每个元素本身为一个列表，其长度与'slot_times'列表一致。该列表中的每个元素代表：若航班被分配至该时刻槽时段，航班所属航空公司需承担的预估总成本，涵盖维修成本、机组人力成本、改签费用、市场价值损失以及宵禁违规成本，单位为2014年欧元。该成本由Mercury模型计算得出，其依据源自文献[3]。 -- 'airlines_flights'：航空公司-航班映射字典，其键为航空公司标识，值为该航空公司所属的航班标识列表。 [1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600 [2] G. Gurtner、L. Delgado与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. 预印本可参见：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