Assessing Interdependencies and Congestion Delays in the Aviation Network

Concerning air traffic delays, air transport networks appear to have variable performance and stochastic nature. A delay incident in one airport may affect the operational efficiency of others and generate various side effects to the whole aviation network. Flight delays are a widespread phenomenon nowadays, costing billions to the air transportation economy and degrading passenger's quality of service. Dependency graphs have been proposed in the past to understand the delay propagation phenomenon and analyze such cascading events by using dependency chains. In this work, we propose a risk-based method to analyze interdependencies and congestions in the aviation network. The methodology and the developed tool can assess delay incidents in airports and produce weighted risk dependency graphs, presenting how a delay that occurred in one airport may affect other interconnected airports. Based on data collected from the US Bureau of Transportation Statistics, we analyze how flight delay risk propagates inside the aviation network. In addition, using historic flight performance data we provide predictions for flight chains, which are prone to delays. We implement a tool that can detect the most critical airports and congested connections based on their delay contribution in dependency chains. It also proposes the n-order dependency chains, which should be avoided by airline flight planners, to reduce delay impacts in the aviation network.