  The National Airspace System (NAS) is a large and complex system with
thousands of interrelated components: administration, control centers,
airports, airlines, aircraft, passengers, etc. The complexity of the NAS
creates many difficulties in management and control. One of the most pressing
problems is flight delay. Delay creates high cost to airlines, complaints from
passengers, and difficulties for airport operations. As demand on the system
increases, the delay problem becomes more and more prominent. For this reason,
it is essential for the Federal Aviation Administration to understand the
causes of delay and to find ways to reduce delay. Major contributing factors to
delay are congestion at the origin airport, weather, increasing demand, and air
traffic management (ATM) decisions such as the Ground Delay Programs (GDP).
Delay is an inherently stochastic phenomenon. Even if all known causal factors
could be accounted for, macro-level national airspace system (NAS) delays could
not be predicted with certainty from micro-level aircraft information. This
paper presents a stochastic model that uses Bayesian Networks (BNs) to model
the relationships among different components of aircraft delay and the causal
factors that affect delays. A case study on delays of departure flights from
Chicago O'Hare international airport (ORD) to Hartsfield-Jackson Atlanta
International Airport (ATL) reveals how local and system level environmental
and human-caused factors combine to affect components of delay, and how these
components contribute to the final arrival delay at the destination airport.
