Flight Delay Prediction using Airport Situational Awareness Map

TL;DR

This paper introduces a novel flight delay prediction method that integrates airport traffic complexity, weather, and flight data using advanced machine learning, significantly improving prediction accuracy.

Contribution

It is the first to incorporate airport situational awareness maps with other data sources for flight delay prediction, enhancing accuracy over previous models.

Findings

Airport traffic complexity significantly impacts delays.

Combining multiple data sources improves prediction accuracy.

Proposed features outperform existing methods.

Abstract

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.