Open Access
ARTICLE
Flight Delay Prediction Using Gradient Boosting Machine Learning Classifiers
Mingdao Lu, Peng Wei, Mingshu He*, Yinglei Teng
Beijing University of Posts and Telecommunications, Beijing, 100876, China
* Corresponding Author: Mingshu He. Email:
Journal of Quantum Computing 2021, 3(1), 1-12. https://doi.org/10.32604/jqc.2021.016315
Received 16 February 2021; Accepted 13 April 2021; Issue published 20 May 2021
Abstract
With the increasing of civil aviation business, flight delay has become
a key problem in civil aviation field in recent years, which has brought a
considerable economic impact to airlines and related industries. The delay
prediction of specific flights is very important for airlines’ plan, airport resource
allocation, insurance company strategy and personal arrangement. The influence
factors of flight delay have high complexity and non-linear relationship. The
different situations of various regions and airports, and even the deviation of
airport or airline arrangement all have certain influence on flight delay, which
makes the prediction more difficult. In view of the limitations of the existing delay
prediction models, this paper proposes a flight delay prediction model with more
generalization ability and corresponding machine learning classification algorithm.
This model fully exploits temporal and spatial characteristics of higher dimensions,
such as the influence of preceding flights, the situation of departure and landing
airports, and the overall situation of flights on the same route. In the process of
machine learning, the model is trained with historical data and tested with the latest
actual data. The test result shows that the model and this machine learning
algorithm can provide an effective method for the prediction of flight delay.
Keywords
Cite This Article
M. Lu, P. Wei, M. He and Y. Teng, "Flight delay prediction using gradient boosting machine learning classifiers,"
Journal of Quantum Computing, vol. 3, no.1, pp. 1–12, 2021.
Citations