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Air traffic complexity is usually defined as difficulty of monitoring and managing a specific air traffic situation. Since it is a psychological construct, best measure of complexity is that given by air traffic controllers. However, there is a need to make a method for complexity estimation which can be used without constant controller input. So far, mostly linear models were used. Here, the possibility of using artificial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artificial neural network configuration. The conclusion is that the artificial neural networks perform as well as linear models and that the remaining error in complexity estimation can only be explained as inter-rater or intra-rater unreliability. One advantage of artificial neural networks in comparison to linear models is that the data do not have to be filtered based on the concept of operations (conventional vs. trajectory-based).

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In this chapter the connection between air traffic complexity and risks in air traffic management system will be explored. Air traffic complexity is often defined as difficulty of controlling a traffic situation, and it is therefore one of the drivers for air traffic controller's workload. With more workload, the probability of air traffic controller committing an error increases, so it is necessary to be able to assess and manage air traffic complexity. Here, we will give a brief overview of air traffic complexity assessment methods, and we will put the traffic complexity assessment problem into a broader context of decision complexity. Human reliability assessment methods relevant to air traffic management will be presented and used to assess the risk of loss of separation in traffic situations with different levels of complexity. To determine the validity of the human reliability assessment method, an analysis of conflict risk will be made based on the real-time human-in-the-loop (HITL) simulations.On the other hand, Cilliers, in his seminal book on the topic, claims exactly the opposite [6]:If a system-despite the fact that it may consist of a huge number of components-can be given a complete description in terms of its individual constituents, such a system is merely complicated. [...] In a complex system, on the other hand, the interaction among constituents of the system, and the interaction between the 3 Air Traffic Complexity as a Source of Risk in ATM DOI: http://dx.doi.org/10.5772/intechopen.90310 system and its environment, are of such a nature that the system as a whole cannot be fully understood simply by analysing its components.

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