@Article{jqc.2021.016315,
AUTHOR = {Mingdao Lu, Peng Wei, Mingshu He, Yinglei Teng},
TITLE = {Flight Delay Prediction Using Gradient Boosting Machine Learning  Classifiers},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {1--12},
URL = {http://www.techscience.com/jqc/v3n1/42587},
ISSN = {2579-0145},
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.},
DOI = {10.32604/jqc.2021.016315}
}