TY - JOUR
T1 - From Single Aircraft to Communities
T2 - A Neutral Interpretation of Air Traffic Complexity Dynamics
AU - Isufaj, Ralvi
AU - Omeri, Marsel
AU - Piera, Miquel Angel
AU - Saez Valls, Jaume
AU - Verdonk Gallego, Christian Eduardo
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/10
Y1 - 2022/10
N2 - At present, decision-making in ATM is fragmented between different stakeholders who have different objectives. This fragmentation, in unison with competing KPAs, leads to complex interdependencies between performance indicators, which results in an imbalance, with some of these indicators being penalized to the apparent benefit of others. Therefore, it is necessary to support ATM stakeholders in systematically uncovering hidden trade-offs between KPAs. Existing literature confirms this claim, but how to solve it has not been fully addressed. In this paper, we envision air traffic complexity to be the framework through which a common understanding among stakeholders is enhanced. We introduce the concept of single aircraft complexity to determine the contribution of each aircraft to the overall complexity of air traffic. Furthermore, we describe a methodology extending this concept to define complex communities, which are groups of interdependent aircraft that contribute the majority of the complexity in a certain airspace. Through use-cases based on synthetic and real historical traffic, we first show that the algorithm can serve to formalize and improve decision-making. Further, we illustrates how the provided information can be used to increase transparency of the decision makers towards different airspace users. In order to showcase the methodology, we develop a tool that visualizes different outputs of the algorithm. Lastly, we conduct sensitivity analysis in order to systematically analyse how each input affects the methodology.
AB - At present, decision-making in ATM is fragmented between different stakeholders who have different objectives. This fragmentation, in unison with competing KPAs, leads to complex interdependencies between performance indicators, which results in an imbalance, with some of these indicators being penalized to the apparent benefit of others. Therefore, it is necessary to support ATM stakeholders in systematically uncovering hidden trade-offs between KPAs. Existing literature confirms this claim, but how to solve it has not been fully addressed. In this paper, we envision air traffic complexity to be the framework through which a common understanding among stakeholders is enhanced. We introduce the concept of single aircraft complexity to determine the contribution of each aircraft to the overall complexity of air traffic. Furthermore, we describe a methodology extending this concept to define complex communities, which are groups of interdependent aircraft that contribute the majority of the complexity in a certain airspace. Through use-cases based on synthetic and real historical traffic, we first show that the algorithm can serve to formalize and improve decision-making. Further, we illustrates how the provided information can be used to increase transparency of the decision makers towards different airspace users. In order to showcase the methodology, we develop a tool that visualizes different outputs of the algorithm. Lastly, we conduct sensitivity analysis in order to systematically analyse how each input affects the methodology.
KW - air traffic complexity
KW - ATM
KW - community detection
KW - graph theory
KW - single aircraft complexity
KW - spatiotemporal indicators
UR - http://www.scopus.com/inward/record.url?scp=85140394026&partnerID=8YFLogxK
U2 - 10.3390/aerospace9100613
DO - 10.3390/aerospace9100613
M3 - Article
AN - SCOPUS:85140394026
SN - 2226-4310
VL - 9
JO - Aerospace
JF - Aerospace
IS - 10
M1 - 613
ER -