Characterizing departure delays of flights in passenger aviation network of United States

TL;DR

This study analyzes large-scale US domestic flight delay data, revealing universal delay propagation patterns and proposing models that could help understand and mitigate systemic delays in passenger aviation networks.

Contribution

It introduces a novel empirical approach using CCDFs and phenomenological models to characterize delay propagation and identify universal metrics across airlines.

Findings

Delay distributions follow shifted power-law and exponential truncated power-law.

Three universal parameters effectively measure airline delay behaviors.

The method offers a new way to analyze temporal big data in transportation networks.

Abstract

Flight delay happens every day in airports all over the world. However, systemic investigation in large scales remains a challenge. We collect primary data of domestic departure records from Bureau of Transportation Statistics of United States, and do empirical statistics with them in form of complementary cumulative distributions functions (CCDFs) and transmission function of the delays. Fourteen main airlines are characterized by two types of CCDFs: shifted power-law and exponentially truncated shifted power-law. By setting up two phenomenological models based on mean-field approximation in temporal regime, we convert effect from other delay factors into a propagation one. Three parameters meaningful in measuring airlines emerge as universal metrics. Moreover, method used here could become a novel approach to revealing practical meanings hidden in temporal big data in wide fields.