Flight Delay Prediction Based on Aviation Big Data and Machine Learning

TLDR

Accurate flight delay prediction is essential for efficient airline operations, yet most existing machine‑learning approaches focus on single routes or airports. The study aims to broaden the factor set influencing flight delay and compare multiple machine‑learning models for generalized delay prediction. The authors constructed a dataset from ADS‑B messages combined with weather, schedule, and airport data, and defined several classification and regression tasks for delay prediction. LSTM can model the aviation sequence data but overfits on the limited dataset, whereas a random‑forest model achieves 90.2 % accuracy on binary classification and mitigates overfitting.

Abstract

Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance-broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2% for the binary classification) and can overcome the overfitting problem.