**1. Introduction**

Increased aircraft demand in air transportation is causing congestion at certain airports and airspace capacities [1]. Moreover, this demand continues to grow year after year. EUROCONTROL has estimated that air traffic will grow by 1.9% per year until 2040 [2]. This increase in aircraft demand creates capacity problems and associated delays, and these effects result in a drop in the efficiency of the air transport system [3]. This situation is already of concern, but is expected to worsen in the coming years. Therefore, improving Air Traffic Management (ATM) efficiency is critical to cope with the increase in aircraft demand [4].

To solve the problems due to the lack of capacity of the Air Traffic Control (ATC) system, the Air Traffic Flow Capacity Management (ATFCM) system arises. Its function is to try to balance the air traffic demand and the capacity of the ATC system at the strategic, pre-tactical, and tactical levels [5]. The ATFCM system proposes short-term and long-term measures to address the effects of the lack of capacity and meteorological uncertainties, and the effectiveness of these measures will depend on the amount, accuracy, and timeliness of the information exchanged [6]. Currently, the most typical ATFCM measure is to limit the entry of certain aircraft into airspace where there are capacity problems. These are the so-called ATFCM regulations. Regulations aim to reduce the workload of Air Traffic Controllers (ATCOs) [7], but they carry associated ground delays concerning the initially planned time of certain aircraft [8]. In recent years, around 50–60% of the total delay in Europe was caused by en-route airspace problems such as en-route capacity or weather [9], reaching even more than 70% in 2018 and 2019 [9]. This makes regulations analysis very relevant and a topic of interest for the industry.

**Citation:** Pérez Moreno, F.; Gómez Comendador, V.F.; Delgado-Aguilera Jurado, R.; Zamarreño Suárez, M.; Arnaldo Valdés, R.M. Prediction of Capacity Regulations in Airspace Based on Timing and Air Traffic Situation. *Aerospace* **2023**, *10*, 291. https://doi.org/10.3390/ aerospace10030291

Academic Editors: Spiros Pantelakis, Andreas Strohmayer and Jordi Pons-Prats

Received: 12 January 2023 Revised: 28 February 2023 Accepted: 14 March 2023 Published: 15 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Specifically, there are up to 14 types of regulations identified by EUROCONTROL [10]. The most common regulations are those due to weather and lack of ATC capacity. The major cause of en-route ATFCM delay is the lack of ATC capacity or ATC staffing [9], meaning that in a part of the airspace, the ATC system cannot cope with the air traffic demand [11]. The weather is also a cause of regulation that causes significant delay. Due to the impact on airport and airspace capacity, and its strong influence on operations [12], adverse weather conditions can lead to large demand-capacity mismatches [13].

This great importance of regulations and their causes means that the study of ATFCM and regulations is arousing interest in the industry [14,15]. Related to this topic, the prediction of regulations due to lack of capacity has been set as the objective of this paper. Thanks to emerging digital technologies, such as [16], the proposed methodology is expected to help improve the efficiency of the ATC system [17]. In this regard, this paper is expected to help manage the ATC system's capacities through prior knowledge of the regulations due to lack of capacity. This way, it is expected that the ATC system will be able to better organise its human and technological resources.

In this paper, our focus is to study only regulations due to capacity. Some studies raise the prediction of regulation during adverse weather conditions [18] or propose solutions such as rescheduling during adverse weather conditions [19]. However, the nature of weather is random and variable [20], being of a different nature to the lack of capacity regulations. For this reason, this is not the subject of study of this paper.

As regulations often arise from imbalances between capacity and demand [21], in this paper, it has been decided to try to predict these regulations. The specific objective is to develop a machine learning model based on historical ATFCM regulations and some information on how air traffic is structured in the sector. This machine learning is thus set to predict regulations due to a lack of capacity.

To achieve this objective, in Section 2 a literature review is presented to see related research on the topic. Then, the methodology developed for the approach of this model is described in Section 3. In addition, an example of the performance and explainability of the developed model under a real operating scenario is described in Section 4. Finally, Section 5 discusses the conclusions obtained after the development of the model and the future steps to be taken in this line of research.

#### **2. Literature Review**

In this section, a review of the literature related to the topic of ATFCM regulations and their prediction will be carried out. ATFCM regulations aim to adapt air traffic demand to the capacity of the ATC system. The regulations have their consequences on an operational tier, but also on an economic tier. In [11], the adverse economic aspects of the regulations are analysed. It is estimated that the total cost can be very large due to the cost of extra fuel, crew cost, or compensation for the regulations themselves. Leading to this, the number of ATFCM regulations should be minimised.

As ATFCM regulations have to be minimised, the study of the causes of re-regulations becomes a priority. According to EUROCONTROL [10], the different causes of these imbalances can be as many as 14. However, in practice, most regulations are caused by the same reasons. Specifically, based on historical data in European airspace in 2019, the distribution of causes of regulation was as stated in Table 1:

**Table 1.** Main causes of ATFCM regulations.


Table 1 shows only those causes with a percentage higher than 1% of the total ATFCM regulations. These causes are mainly due to the lack of operational capacity or the weather. Therefore, although the EUROCONTROL studies estimate that there are many causes, there are two main causes that are responsible for ATFCM regulations: Lack of capacity and climate.

Climate is the most unpredictable factor. Some studies make analyses related to climate, such as [20], where a model is developed to predict trajectories when there are weather uncertainties, or [18], where a prediction of aircraft in the sector and regulations in adverse weather conditions is made. However, this variability makes weather regulations very difficult to analyse and estimate. In [22], a study is carried out on the possibility of estimating weather regulations. In this reference, a machine learning model based on components such as wind, humidity, and temperature is used. Although the results seem to be positive, there is still a lot of work to be completed.

On the other hand, the study of capacity regulation is more widespread. There is a belief that it will be possible to estimate, and therefore anticipate, capacity regulations and their effects. For this reason, more studies are being carried out on this subject.

Some articles focus on studies of the influence of capacity regulation, such as [23]. This paper develops a study of the applicability of machine learning models to predict the delay caused by capacity regulations. This research concludes that it is beneficial to use data-driven machine learning models to predict these delays, rather than using causal relationships.

Another article that focuses on the study of capacity regulations is [14]. Here, a study is made of how capacity regulations can help reveal restorative mechanisms for tactical planning. A methodology for defining network states has been developed based on these capacity regulations.

In addition to the analysis of the capacity regulations themselves, there is also interest in their prediction by allowing the ATFCM service to anticipate their effects. This line of research is represented by [24], where a machine learning model is developed that is capable of estimating capacity regulations using variables such as the capacity itself, the number of aircraft, or the expected workload of the controllers.

This research is currently gaining importance due to the development of machine learning models and the increasing imbalance between capacity and demand. Therefore, this paper is in this line. The aim of this paper is the same as the one of [24]. Both papers attempt to predict capacity regulations. Therefore, the objective is different from the rest of the publications analysed:


Therefore, from the publications analysed, the only one that shares this objective is [24]. The main difference is the scope of the model, and thus the composition. The objective of [24] is the prediction of regulations by capability but on a tactical or pre-tactical time horizon. In this publication, the scope is to predict regulations in a strategic horizon. This makes the theoretical background, and therefore the machine learning models developed, different, and even complementary.

After a review of the literature, one can also conclude the contributions of this paper to the prediction of regulations.

This paper aims at predicting regulations, as the other analysed papers. However, it tries to predict capacity regulations by means of a different and novel approach. Previous models are based solely or mainly on what the traffic is like at the time of prediction or what it is expected to be like. This is important, but the regulations will depend on many external aspects such as the situation of the ATC system. From an operational point of view, the time component is very important, as here, behaviour patterns can be found that do not depend solely on traffic.

In addition, the traffic flow distribution also allows for studying the influence of traffic without taking into account each individual aircraft. This will make it possible to find behavioural patterns in the traffic structure in general.

Finally, this model for predicting regulations will allow progress to be made in the study and prediction of their effects, which is what is really important. From an operational point of view, in a control room, 10-min time ranges can be used to evaluate the possible effect of the regulation, since the final real delay is always different from the ATFCM delay that an aircraft has, and this is seen in 10-min periods in order to have a margin to estimate this difference. Therefore, from an operational point of view, the 10-min window is used as the analysis parameter. Therefore, the approach followed has been based on real operational knowledge and will also bring these investigations closer to real operation.
