| dc.description.abstract | Flight delays can be caused by many reasons. Some factors are controllable such as factors relating to airlines’ factors, airport ground handling, aircraft maintenance, improper flight scheduling. On the other hand, there are some uncontrollable factors, such as weather, air traffic control, mechanical failure. For the related studies of flight delays, very few explore the use of data mining methods. This research focuses on an airline corporation and the main factors to the cause of the delay of Taipei flight are collected from 2004 to 2014 as the dataset. Data mining techniques are used to discover useful information about flight delays and can provide some guidelines for the company and academia about the delay factors.
The experiments were conducted by WEKA3.6.10. The information focuses on annual departure of airlines from 2004 to 2013, and the Class Label design is based on the flight delay. In addition, two feature selection methods are used to select representative features from the dataset, which are information gain and the genetic algorithm. The decision trees (C4.5 and CART), support vector machine (SVM), and multiple classifiers by bagging and boosting are developed as the prediction models for comparison. Furthermore, the data of 2014 are used to validate some better prediction models.
Our research has evidently showed that using the training data of 2004 flight information and highly predictable model is the most accurate research method. The increased quantity of the data and the performances of the prediction methods have presented contrasting results, which means that higher quantity data will result in the loss of the predictability of the airlines. According to the incorrect prediction of airline delays, our logical explanation has concluded that when the delayed of flights has been incorrectly predicted, it results in the massive loss of production cost. This research has identified the better prediction models of flight delays for the airline companies. We have found that the greatest cause of the delayed of airlines based on our prediction models is due to the lack of regular maintenance on the machineries. We should perform regular machinery check-ups and reorganize airline schedules in order to prevent future accidents and effectively reduce the operation time and flight delayed time.
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