Naslov
Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks

Autori
Andraši, Petar ; Radišić, Tomislav ; Novak, Doris ; Juričić, Biljana

Izvornik
Promet - Traffic & transportation (0353-5320) 31 (2019), 4; 377-386

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Air Traffic Complexity, Complexity Estimation, Artifical Neural Network, Generic Algorithm, Human-in-the-loop Simulation, Air Traffic Management

Sažetak
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 com- plexity 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 arti-ficial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artifi-cial 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. trajec- tory-based).

Izvorni jezik
Engleski

Znanstvena područja
Tehnologija prometa i transport

POVEZANOST RADA