Operational Aspects of Vertical Navigation during the Approach Phase of Flight: CDA vs. Conventional Step-Down Approach

The continuous descent approach (CDA) is an operational technique used by aircraft when descending from cruise altitude; the aim is to minimize thrust and thereby avoid horizontal flight segments. CDA involves vertical navigation calculations that modify flight trajectory according to altitude; these procedures can reduce fuel consumption, emission of toxic exhaust gases, and noise due to the aircraft and its engines. In order to verify some of these benefits under field conditions in Croatia, the present study analysed fuel consumption, approach distance and approach duration during 44 landings by Croatia Airlines Dash-8 Q400 aircraft at the airport in Split, Croatia. CDA was performed at 426 km/h (230 knots) or at high speed, and these procedures were compared with the standard step-down approach involving a flight speed of 426 km/h (230 knots) and an18.5 km-long (10 NM) horizontal segment at an altitude of 914 m(3000 ft). The different approach conditions were compared in terms of fuel consumption. The results indicate that implementing CDA can provide small fuel savings on individual flights, and that these savings can be significant when calculated over an entire fleet on an annual basis. The significant reduction in fuel consumption should also mean a reduction in CO2 emissions

The Proposal of a Concept of Artificial Situational Awareness in ATC

Automation is one of the most promising solutions to the airspace capacity problem. However, we believe that in order to safely implement advanced automation concepts in air traffic control, it is necessary for AI and humans to share situational awareness. One of the main objectives of this concept proposal is to explore the effects and possi-bilities of distributed human-machine situational awareness in en-route air traffic control operations. Instead of automating isolated individual tasks, such as conflict detection or coordination, we propose to create a basis for automation by developing an intelligent situation-aware system. The sharing of the same situational awareness be-tween the members of the air traffic controller team and AI enables the automated system to reach the same conclu-sions as air traffic controllers when faced with the same problem and to be able to explain the reasons for these conclusions. Machine learning can be used to predict, estimate and filter at the level of individual probabilistic events, an area in which it has shown great ability so far, whereas the reasoning engine can be used to represent knowledge and draw conclusions based on all the available data and explain the reasons for these conclusions. In this way, the artificial situational awareness system will pave the way for future advanced automation based on machine learning. Here, we will explore which technologies and concepts are useful in building the artificial situational awareness system and propose the methodology for testing the AI situational awareness

Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks

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).</p

The Proposal of a Concept of Artificial Situational Awareness in ATC

Automation is one of the most promising solutions to the airspace capacity problem. However, we believe that in order to safely implement advanced automation concepts in air traffic control, it is necessary for AI and humans to share situational awareness. One of the main objectives of this concept proposal is to explore the effects and possi-bilities of distributed human-machine situational awareness in en-route air traffic control operations. Instead of automating isolated individual tasks, such as conflict detection or coordination, we propose to create a basis for automation by developing an intelligent situation-aware system. The sharing of the same situational awareness be-tween the members of the air traffic controller team and AI enables the automated system to reach the same conclu-sions as air traffic controllers when faced with the same problem and to be able to explain the reasons for these conclusions. Machine learning can be used to predict, estimate and filter at the level of individual probabilistic events, an area in which it has shown great ability so far, whereas the reasoning engine can be used to represent knowledge and draw conclusions based on all the available data and explain the reasons for these conclusions. In this way, the artificial situational awareness system will pave the way for future advanced automation based on machine learning. Here, we will explore which technologies and concepts are useful in building the artificial situational awareness system and propose the methodology for testing the AI situational awareness

Air Traffic Complexity as a Source of Risk in ATM

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