The prediction of flight delay: Big data-driven machine learning approach

Jiage Huo, K. L. Keung, C. K.M. Lee, Kam K.H. Ng, K. C. Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538672204
Publication statusPublished - 14 Dec 2020

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X


  • Big Data
  • Flight Delay
  • Machine Learning
  • Prediction

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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