Prediction of Air Flight Cancellation during COVID-19 using Deep Learning Methods

Abstract

Air traffic is vulnerable to external factors, such as oil crises, natural disasters, economic recessions and disease outbreaks due to COVID-19. This reason seems to have a more severe and more rapid impact on air traffic numbers as sudden increases in flight cancellations, aircraft groundings and travel bans. Various Airways loose revenues and it is difficult for them to sustain for a long period. This problem as been facing the entire world. The reductions in passenger numbers are significant. It is due to flights being cancelled or planes flying empty between airports. It is in turn massively reducing revenues for airlines and forced many airlines to lay off employees or declare bankruptcy. Airways also have to attempt refunding cancelled trips in order to diminish their losses. The airliner manufacturers and airport operators have also laid off employees. According to some commentators, this crisis is the worst ever encountered in the history of the aviation industry.

Aircraft cancellation prediction is accomplished by utilising deep learning framework. In this framework, two dissimilar recurrent neural networks are assembled as a single entity while inferring the prediction results. Long-short term memory (LSTM) and Gated Recurrent Unit (GRU) are employed to design the proposed predictive model. This predictive model is compared against traditional neural network based Multi-layer perceptron model. Experimental results indicated an accuracy of 98.7% by the proposed model.

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 12 September 2020

Affiliations

[1 ] GLA University

[2 ] The Bhawanipur Education Society College

Article

DOI: 10.14293/S2199-1006.1.SOR-.PPB0TJS.v1

License:

This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

Funding

GLA University Research Grant to Teachers