Interplay of network topologies in aviation delay propagation: A complex network and machine learning analysis

Abstract

In this study, the fundamental characteristics of flight delay propagation and the key factors influencing such propagation are investigated. Three distinct types of networks were constructed: an aviation network, a traffic flow network, and a delay propagation network. Employing complex network theory, an analysis of the fundamental topological attributes of each network was conducted, exploring the interrelations among these attributes. Findings reveal a notable resemblance between the network topology attributes of the delay propagation network and the traffic flow network. The delay propagation network overall adheres to scale-free network attributes, with a few dominant major airports playing a pivotal role in the delay propagation process. Furthermore, it was uncovered that smaller airports are more susceptible to the influence of delay propagation than their larger counterparts. Delays, in a general sense, exhibit a pronounced tendency to aggregate and spread extensively within the realm of smaller airports. Regarding delay propagation, airport traffic emerges as the primary factor precipitating this phenomenon, with a feature importance score reaching 0.889. An escalation in airport traffic significantly amplifies the extent of delay propagation, yet concurrently, this escalation is not incessant but stabilizes beyond a certain threshold.