## 2 State of the Art  
Air Traffic Management (ATM) needs to be improved in the wake of significant growth and variations in traffic [13]. A new regulatory framework could enhance safety, cost and flight efficiency; an elastic economic regulatory system could also enable capacity and react faster to changes in demand. Such a new ATM system would enhance the Green Deal measures [14]. Single European Sky ATM Research (SESAR) was created in 2008 due to increasing air traffic and rational delays since 2000. Its fourth pillar establishes the management of air transport capacity. In 2018, the Airspace Architecture Study (AAS) [15] identified possible solutions for capacity and demand imbalance such as arrival management and improved aviation meteorology. In [16], two main challenges in the Main Plan (MP) are foreseen: environmental concerns and a mismatch between traffic demand and ATM capacity. Delays can be due to various reasons including weather conditions [17], ground delays, runway queues and capacity constraints [18], and delays are a major source of direct and opportunity costs [19]. A review of different approaches to flight delay prediction and how this problem is addressed is presented in [20]. They compare the prediction models used, such as operational research [21], machine learning [22], Bayesian network approach [23], probabilistic models, statistical analysis, a super statistical approach [24] and ensemble methods and select representative algorithms [25]. A novel predictive model applying graphs to sequence learning architecture is studied in [26]. Authors in [27] affirm that comparing flight schedules and flight plans is a very useful way of locating flight delay occurrences and modelling flight delays. Air traffic network efficiency depends on strikes [28] and arrival processes [29] among other factors. The Arrival Manager (AMAN) seeks to improve the flow when capacity constraints exist, so the system needs reliable assessment and estimations of delay and capacity. All this translates into additional miles flown due to cancellations, delays or rerouting of scheduled flights, increasing horizontal flight distance and thus affecting fuel consumption, environmental factors and costs to customers and airlines [30]. An analysis of the insights on the estimated climate costs of the aviation sector due to air management for 2018 and 2019 is presented in [31] and found them to be as high as 1 bn EUR. Other authors study the expected costs for airspace users as a result of Air Traffic Management (ATFM) regulations [32]. ANSPs, airlines, planners and regulators manage imbalances between short- and long-term demand and air capacity in different FABs. Recently, the Eurocontrol Network Operations Plan (NOP) realised that traffic flow predictions are not as accurate as they used to be because of sharp peaks in demand, which make it difficult to apply enough capacity. This situation is exacerbated in core areas of Europe such as FABEC, where 60% of airlines are flying longer and more expensively [2]. As a result, a new term has become very familiar in ATM: volatility. This refers to unexpected changes in the number of flights. It seems to be a useful indicator that can further understanding of the balance between demand and capacity, i.e., traffic variations in time and space. Volatility depends on seasonality, weather forecasts, the closing of airspace due to geopolitical decisions, strikes, airline decisions, unexpected Air Traffic Charges (ATC), service charges and economic cycles. A fuzzy cognitive map of 39 concepts to analyse the links between them and estimate the causes and effects of volatility is drawn in [1]. [PERSON] et al. conducted a literature review of volatility in air transport, identified factors that influence it and suggested strategies for addressing it [33]. However, they do not mention volatility as a measure of flight fluctuation. The reference [34] shows that the path and cycle approach is a reasonable way of modelling this hotspot problem.