## 4 Design Space: Trends of AI in ATM
### Categorisation Insights  
The primary Objective of the model already categorise successfully the different AI models used in ATM. The different models used in the different categories are instead presented in the following, using selected references--please refer to Table 4 for the description of the descriptors. AI_Prediction_ in ATM is performed using a vast range of AI models, with most used utilised being: (i) Multi-Agent Systems (MAS); (ii) Neural Network (NN); (iii) Random Forest (RF); (iv) Gradient Boosting Machine (GBM); (v) Support Vector Machine (SVM); and (vi) Linear Regression. The five later models--NN, RF, GBM, SVM, and linear regression--are mostly used to predict: (a) A descriptor of the State or an Indicator of the Trajectory of an aircraft, e.g., mass estimation [34], descent length [35], phase of flight [36]; and (b) a Ground Traffic Indicator or a State descriptor of an Airport, e.g., the estimated  
Figure 8: Evolution of the number of publication per year of the Prediction, Optimisation/Automation, Modelling/Simulation, and Analysis categories. Figure 9: Distribution of the publications in function of the categories. take-off time [37], or taxi speed [38]. Authors using these models are mostly capitalising on framework availability of the past years and are often used jointly for comparison, with the linear regression often used as a baseline. Nonetheless, (ii)-(v) AI models have also been used for other type of predictions such as Route choice [39], the Structure of a Sector configuration [40], the Environmental State of the Airspace, ATCO action prediction [41], or--short-term--4D Trajectory prediction [42; 43]. Multi-Agent Systems on their side have been used to model and predict more complex tasks, like Indicators of the Traffic such as delay propagation on networks [44], 4D Trajectory, and to a certain extent, 5D Traffic prediction [45] and CTR Traffic--CTR Traffic being easier to predict due to the important amount of constraints. AI _Optimisation_ and _Automation_ works use mostly a more restricted range of AI models: (i) Multi-Agent Systems (MAS); (ii) Evolutionary Algorithm (EA), mostly genetic algorithms; (iii) Simulated Annealing (SA); and (iv) Reinforcement Learning (RL) (e.g., RL, multi-agent RL, Deep RL, and DQN, see Table 5). A majority of these works focus on optimising the traffic and/or avoiding collisions, from the point of view of the trajectories. 5D Traffic Optimisation works focus in general on one flight phase, such as optimising (a) EnRoute traffic, using centralised, i.e., SA [46] or EA [47], or decentralised, i.e., MAS [48; 49]; (b) arrival traffic [50]; (c) departure traffic [51]; (d) Ground Traffic (\"5D\" Traffic) Optimisation--although this traffic is not really using altitude, it is still noted 5D Traffic in the categorisation to avoid confusion between traffic and trajectory--; or (e) the whole CTR Traffic [52]. Notable other focus of AI model for optimisation are optimising Airspace Structure, such as Route network [53], Sectors [54], and Optimising a 4D Trajectory [55], optimising the Demand/Capacity Balance, or the Route of an Aircraft. Analysis of ATM activities using an AI model is mostly composed of: (i) Techniques that clusterise--e.g., DBSCAN, BIRCH, or auto-encoder NN--4D Trajectories in order to analyse the different factor influencing the 4D Trajectory, the Route choice [56], the CTR Traffic, in particular the arrivals [57], but also Traffic Indicators such as delays [58], in order to understand the different influencing factors and/or as a first analysis to latter use in a predictive model; or (ii) more precise analysis, such as trajectory analysis to detect ATCOs action [59], or speech recognition and analysis or utterance of ATCOs [60] or a Pilot. AI _Modelling_ in ATM is mostly performed using multi-agent systems, which is not a surprise considering their importance in simulation in many domains, such as the simulation of car traffic.