Affiliation:
1. Tibet University
2. Southeast University
3. Southeast University, School of Transportation
Abstract
<div class="section abstract"><div class="htmlview paragraph">Nowadays, the rapid growth of civil aviation transportation demand has led to
more frequent flight delays. The major problem of flight delays is restricting
the development of municipal airports. To further improve passenger
satisfaction, and reduce economic losses caused by flight delays, environmental
pollution and many other adverse consequences, three machine learning algorithms
are constructed in current study: random forest (RF), gradient boosting decision
tree (GBDT) and BP neural network (BPNN). The departure flight delay prediction
model uses the actual data set of domestic flights in the United States to
simulate and verify the performance and accuracy of the three models. This model
combines the visual analysis system to show the density of departure flight
delays between different airports. Firstly, the data set is reprocessed, and the
main factors leading to flight delays are selected as sample attributes by
principal component analysis. Secondly, the mean absolute error (MAE), mean
absolute percentage error (MAPE) and root mean square error (RMSE) were selected
as evaluation indexes to compare the prediction results of three different
models. The final results show that the departure flight delay prediction model
based on BPNN algorithm has faster solution speed and overcomes the over-fitting
problem, and has higher prediction accuracy and robustness. Based on the
algorithm developed in this paper, the airport system can be planned in a
targeted manner, thereby alleviating the pressure of air transportation and
reducing flight delays.</div></div>
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