## 3 Bibliometric Analysis
### Science Mapping  
The last analysis prior to the identification of relevant contributions is the science mapping analysis. This analysis is carried out with the aim of identifying recurrent topics in publications related to air traffic complexity. With this, the question \"What have been the key issues studied related to air traffic complexity?\" can be answered. By combining the topics that are considered relevant, together with the information and conclusions obtained from the performance analysis, it is possible to determine which publications have been the most prominent on this topic. The science mapping in this case consists of a co-occurrence analysis of words carried out by the VOSviewer tool. All keywords appearing three or more times in the set of analysed publications were identified. They were then structured in the form of a network according to the relationships between these words. Figure 9 shows the result of this analysis. A total of 31 words occurring three or more times were obtained. In Figure 9, the words are divided into clusters, which are represented in different colors. Words stand out, such as: \"complexity\" and \"air traffic control\" in yellow, \"workload\" in purple, \"model\" and \"conflict detection\" in green, \"air traffic complexity\" in red, \"air traffic management\" in blue, and \"time-series\" in orange. From these clusters, the main words of the whole network can be identified. For a more detailed overview of the network, information on the 10 most common keywords has been added. This information is in Table 3. Additionally, a summary of each of the clusters in the network has been added to Table 4. This was carried out to have a more detailed understanding of the entire network and not only to focus on  
Figure 9: Network of co-occurrence of keywords. the keywords that are globally more important. Specifically, the number of keywords in each cluster and the keyword of each cluster are specified. In Table 3, two of the most relevant terms are \"Air Traffic Control\" and \"Air Traffic Management\". This is because of the direct relationship of the concept of complexity with the ATC service and the workload of air traffic controllers [44]. The terms \"Complexity\" and \"Air Traffic Complexity\" are general terms related to the topic. \"Conflict Detection\" is also related to complexity, as there is an increase in complexity and an increase in the workload of the controllers, so the detection and potential resolution of conflicts also need to be addressed. The term \"model\" refers to the fact that, in different publications analysing complexity, the study is based on the definition of a complexity indicator, or a model used to measure complexity. With this study, the topics of greatest interest in this area of knowledge can be determined. Additionally, Figure 10 shows the same co-occurrence network, but shows its evolution by years from 2014 to 2020. In this network, the same keywords can be observed as in Figure 9. However, different trends can be seen in the evolution over time. In the first few years of this study, the keywords were, among others, \"workload\", \"mental workload\", or \"air traffic control\". This indicates that, in the first few years analysed, the studies focused more on human factors and on studies of complexity related to the workload of controllers. In 2020, some of the keywords are \"machine learning\", \"air traffic flow management\", or \"uncertainty\". This indicates that the trend has changed, and that the complexity problem is now approached from a point closer to air traffic flow management and artificial intelligence. This variation in the trend, together with the previous co-occurrence analysis of words, allows us to complete the performance analysis and to be able to know the different topics of major interest within the air traffic complexity studies. With this, it is possible to answer the following question:  
What have been the key issues studied related to air traffic complexity?