Timescales of delay propagation in airport networks

Abstract

Flight delays persist and spread in airport networks due to high interconnectivity in the air transportation infrastructure. How quickly delay propagates between two airports is determined by factors such as the number of flights between airports, the duration of the flight, presence of disruptions, and schedule buffers. Accurate estimation of the time for delay propagation can improve system predictability and reliability. However, noisy airport delay data, along with a lack of visibility into airline scheduling and disruption management strategies, result in a challenging estimation problem for such propagation timescales. We present an algorithm to estimate statistically significant time lags between airport delays from noisy, aggregate operational data. The algorithm uses sliding correlation windows to extract the airport pairs with stable delay lags. We apply our method to identify different timescales of interactions for US airport delays in 2017. Our analysis yields two main results: (1) The most stable lags between airport delays involve the Northeast airports; (2) The stable lags between two airports are negatively correlated to the scheduled flight times between the same two airports. These results regarding delay propagation speeds have potential implications for delay prediction models and airline schedule design.