Flight delay predictions and the study of its causal factors using machine learning algorithms

Cho Yin Yiu, Kam K.H. Ng, Kin Chung Kwok, Wing Tung Lee, Ho Tung Mo

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

Abstract

In recent years, the global civil aviation industry has been developing rapidly. Due to the rising demand for air transportation, airports are confronting saturation problems. Heavy traffic and long queues are expected for take-off and landing. Hence, the physical constraints have magnified the problem of having surging flight delays. Yet, the operational efficiency and the reputation of the airport will deteriorate if the delay propagates. Additional expenditures are also expected. Several machine learning approaches were adopted in this research to predict flight delay, including the decision tree, random forest, k-nearest neighbour, Naive Bayes, and artificial neural networks. The results show that all algorithms achieved more than 80% of accuracy and artificial neural networks perform the best among the alternatives. While Naive Bayes is the least accurate, k-nearest neighbour have the lowest Fj score.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021
EditorsHuabo Sun
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages179-183
Number of pages5
ISBN (Electronic)9781665425186
DOIs
Publication statusPublished - Oct 2021
Event3rd IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021 - Changsha, China
Duration: 20 Oct 202122 Oct 2021

Publication series

NameProceedings of 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

Conference

Conference3rd IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021
Country/TerritoryChina
CityChangsha
Period20/10/2122/10/21

Keywords

  • Flight delay
  • Forecasting
  • Machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management
  • Aerospace Engineering
  • Safety, Risk, Reliability and Quality
  • Instrumentation
  • Safety Research

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