Flight delay prediction using airport situational awareness map

Abstract

The prediction of light delays plays a signiicantly important role for airlines and travellers because light 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 light. Diferent from previous work, we are the irst group, to our best knowledge, to take advantage of airport situational awareness map, which is deined as airport trafic complexity (ATC), and combine the proposed ATC factors with weather conditions and light 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 signiicant impact on light departure delay time. The prediction results show that the proposed factors are the main reasons behind the light delays. Using our proposed framework, an improvement in accuracy for light departure delay prediction is obtained.