  The prediction of flight delays plays a significantly important role for
airlines and travelers because flight delays cause not only tremendous economic
loss but also potential security risks. In this work, we aim to integrate
multiple data sources to predict the departure delay of a scheduled flight.
Different from previous work, we are the first group, to our best knowledge, to
take advantage of airport situational awareness map, which is defined as
airport traffic complexity (ATC), and combine the proposed ATC factors with
weather conditions and flight information. Features engineering methods and
most state-of-the-art machine learning algorithms are applied to a large
real-world data sources. We reveal a couple of factors at the airport which has
a significant impact on flight departure delay time. The prediction results
show that the proposed factors are the main reasons behind the flight delays.
Using our proposed framework, an improvement in accuracy for flight departure
delay prediction is obtained.
