Reduction of Air Traffic Complexity Using Trajectory-Based Operations and Validation of Novel Complexity Indicators

Radišić, Tomislav; Novák, Doris; Juričić, Biljana

doi:10.1109/tits.2017.2666087

Cited by 20 publications

(25 citation statements)

References 7 publications

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“…This Section presents a brief overview of the experiment which produced data for training artificial neural networks (ANNs). The details of the experiment and analysis can be found in [14] and [15].…”

Section: Methodsmentioning

confidence: 99%

“…ARTIFICIAL NEURAL NETWORKS Figure 1 -Relationship between ATC complexity and workload [4] complexity factors, the Dynamic Density performance could be improved by using non-linear techniques such as non-linear regression, genetic algorithms, and neural networks, which is exactly what was tested in this paper. In our previous work, similar approach was used to test the effect of the trajectory-based operations (TBO) on air traffic complexity [14]. ATCOs were recruited to perform human-in-the-loop (HITL) simulations during which they were asked to provide real-time assessment of air traffic complexity.…”

Section: Subjective Air Traffic Complexity Estimation Usingmentioning

confidence: 99%

“…For this research the complete vertical expanse was used. Most flights flying through this sector are over-flights while minority of flights depart or arrive in Croatia [14].…”

Section: Apparatusmentioning

confidence: 99%

“…It also states what the coordination points (COP) or transfer of control (TOC) points are. The participants were required to adhere to these procedures during the simulation runs [14].…”

Section: Apparatusmentioning

confidence: 99%

“…An analysis was made of historic traffic data to determine the aircraft type distribution and most commonly used routes. The results of this analysis were used to make the traffic which was generated for future-traffic scenarios more representative than the actual traffic [14].…”

Section: Apparatusmentioning

confidence: 99%

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Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks

Andraši¹,

Radišić²,

Novák³

et al. 2019

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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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Section: Methodsmentioning

confidence: 99%

Section: Subjective Air Traffic Complexity Estimation Usingmentioning

confidence: 99%

“…For this research the complete vertical expanse was used. Most flights flying through this sector are over-flights while minority of flights depart or arrive in Croatia [14].…”

Section: Apparatusmentioning

confidence: 99%

“…It also states what the coordination points (COP) or transfer of control (TOC) points are. The participants were required to adhere to these procedures during the simulation runs [14].…”

Section: Apparatusmentioning

confidence: 99%

Section: Apparatusmentioning

confidence: 99%

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