PNNL

Abstract

It is well-known that delays and disruptions that originate at certain airports end up spreading spread on the network to other airports, sometimes straining a large fraction of the system. Hence data-driven modeling of this phenomenon is important from the standpoint of planning and strategy. In this work, utilizing the publicly available data from the Federal Aviation Administration (FAA), we employ the well-known influence maximization paradigm, to mine for the top airports in the US airport network, from the perspective of delay spread. In the process, we develop a diffusion simulator and implement a greedy algorithm to aid our task. We then explore the temporal trends by considering one winter month and one summer month in our analysis. Finally, we characterize the clusters in the US airport network and investigate the patterns of delay spread when airports in each of the clusters are affected.