## 4 Identification and Summary of Main Contributions
The main advantage of this model is the non-linear dynamic approach followed by the model, which allows for both average and local complexity indicators. The limitation of the model is that it assesses air traffic complexity simply by modelling aircraft trajectories, without taking other aspects into account. Air traffic complexity, although largely dependent on aircraft density, is a parameter that has to take into account a larger number of parameters, such as the number of aircraft, the mix of aircraft models, meteorology, separation between aircraft, aircraft speed, and regulations affecting traffic [49]. Ref. [11] provides a review of the complexity indicators that have been used up to the time of publication. It summarises the indicators \"Aircraft Density\", \"Dynamic Density\", \"Interval Complexity\", \"Fractal Dimension\", \"Input/Output Approach\", and \"Lapunov \begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Citations** & **Main Topic** \\ \hline
[45] & 41 & Sector complexity maps, defined as the response to disturbances \\
[46] & 21 & Defining complexity through dynamic system modelling \\
[11] & 72 & Review of complexity indicators \\
[47] & 25 & Study of the complexity of air traffic based on the study of complex networks \\
[48] & 21 & Complexity prediction using machine learning models \\ \hline \end{tabular}
\end{table} 
Table 5: Preliminary analysis of main contributions on air traffic complexity. Exponents\". The author discusses the differences between them and the main features of each. The author states that these models have served as a reference for progress in this field in previous years. But, these models do not consider aspects such as uncertainty in aircraft trajectory, or the increasing automation of ATM. She, therefore, speaks of the need to continue defining more modern complexity indicators, but considering the progress already made. Additionally, Ref. [47] carries out a study of the complexity of air traffic based on the study of complex networks and graph theory, also treated in [50]. These complex networks are based on the definition of certain variables of interest and their relationship with each other to establish a complex system that is more than the sum of its parts. Using this approach, this article assesses complexity in two and three dimensions, dividing it into low, medium, and high tiers. The temporal assessment of complexity is also evaluated. Conclusions are drawn on how this complexity evolution can influence the controllers. The main advantage of this model is that the classification of the scenarios is performed by artificial intelligence and human subjectivity can be avoided, and that the complex network approach allows several different variables to be related. The limitation is that the number of variables is reduced to the number of aircraft and the changes in aircraft trajectories in a congested space. Aspects such as the regulations that may appear in the airspace [51], which are one of the main causes of delays, are not considered in this model. Finally, the analysis of [48] is performed. This publication performs complexity prediction using machine learning models. In this paper, scenarios of low, medium, or high complexity are classified. This classification is carried out using ATM experts. Subsequently, a machine learning model is built that learns from this sample and can predict future scenarios of low, medium, or high complexity based on common characteristics. Training is also performed with unclassified samples to have a more robust model. For this model, the authors evaluated the complexity of the airspace through the Sector Operation Complexity (SOC) based on the complexity of a sector to be controlled. This indicator is used in several publications [14; 52]. The authors conclude that this tool can be used as an auxiliary in the decision-making process of a real ATM tool.