A Model-Based Approach for Flight Delay Prediction

Abstract

Flight delays incur costs directly and indirectly, such as for maintaining crowd at the gate, extra remuneration for staff, food service, and lodging. The delayed arrival of aircraft will have significant impact on an airport's management, like the reallotment of parking gates, runways and scheduling of ground staff. The precise prediction of flight delays can provide passengers with dependable travel schedules and improve the service performance of airports and airlines. Objectives: To propose a model that can predict a flight delay, using machine learning algorithms like logistic regression methods. Methods: Logistic model with and without shrinkage was fit on an Airlines dataset of movements of 6585 domestic flights of 18 airlines in the United States and then, lasso, ridge and elastic net were used to improve the predictive ability. Results: Among all the models, the day-specific was found to give the best flight delay prediction. Conclusion: Since, only 0.6 sensitivity and 0.5-0.6 specificity could be achieved in our predictive model, it is not very reliable as there are other factors that influence a flight delay.

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Reproductions