  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.
