Quantifying delay propagation in airline networks

We develop a framework for quantifying delay propagation in airline networks that combines structural modeling and machine learning methods together to estimate causal objects of interest. Using a large comprehensive data set on actual delays and a model-selection algorithm (elastic net) we estimate a weighted directed graph of delay propagation for each major airline in the US and derive conditions under which the estimates of the propagation coefficients are causal. We use these estimates to decompose the airline performance into “luck” and “ability.” We find that luck may explain about 38% of the performance difference between Delta and American in our data. We further use these estimates to describe how network topology and other airline network characteristics (such as aircraft fleet heterogeneity) affect the expected delays.