Flight Delay Prediction Based on Aviation Big Data and Machine Learning

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 1; pp. 140 - 150
• Main Authors: Gui, Guan, Liu, Fan, Sun, Jinlong, Yang, Jie, Zhou, Ziqi, Zhao, Dongxu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: ADS-B; Airport design; Airports; Atmospheric modeling; Aviation; Classification; Datasets; Delay; Delays; Flight delay prediction; LSTM neural network; Machine learning; Meteorology; Model accuracy; Predictive models; random forest; Schedules; Task analysis; Task scheduling; Weather
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2019.2954094

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.