@inproceedings{b8259447a272419f9562bf458b3f120b,
title = "The prediction of flight delay: Big data-driven machine learning approach",
abstract = "Nowadays, Hong Kong International Airport faces the issues of saturation and overload. The difficulties of selecting taxiways and reducing the lead time at the runway holding position are the severe consequences that appeared from increasing the number of passengers and increased cargo movement to Hong Kong International Airport but without constructing a new runway. This paper is primarily about predicting flight delays by using machine learning methodologies. The prediction results of several machine learning approaches are compared and analyzed thoroughly by using real data from the Hong Kong International Airport. The findings and recommendations from this paper are valuable to the aviation and insurance industries. Better planning of the airport system can be established through predicting flight delays. ",
keywords = "Big Data, Flight Delay, Machine Learning, Prediction",
author = "Jiage Huo and Keung, {K. L.} and Lee, {C. K.M.} and Ng, {Kam K.H.} and Li, {K. C.}",
note = "Funding Information: This work was supported in part by the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, in part by the Interdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China. Our gratitude is also extended to the Research Committee and the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China, and The Innovation and Technology Commission, The Government of the Hong Kong SAR, Hong Kong for support of this project (PRP/002/19FX/K.ZM31). Our gratitude is also extended to the Research Committee and the Interdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University for support of the project (BE3V). The authors would like to express their appreciation to the Hong Kong International Airport and FlightGlobal for their assistance with the data collection. Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2020",
month = dec,
day = "14",
doi = "10.1109/IEEM45057.2020.9309919",
language = "English",
series = "IEEE International Conference on Industrial Engineering and Engineering Management",
publisher = "IEEE Computer Society",
pages = "190--194",
booktitle = "2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020",
address = "United States",
}