Airline Flight Delays using Artificial Intelligence in COVID-19 with Perspective Analytics

What is it about?

This study envisages assessing the effects of the COVID-19 on the on-time performance of US-airlines industry in the disrupted situations. The deep learning techniques used are neural network regression, decision forest regression, boosted decision tree regression and multi class logistic regression. The best technique is identified. In the perspective data analytics, it is suggested what the airlines should do for the on-time performance in the disrupted situation. The performances of all the methods are satisfactory. The coefficient of determination for the neural network regression is 0.86 and for decision forest regression is 0.85, respectively. The coefficient of determination for the boosted decision tree is 0.870984. Thus boosted decision tree regression is better. Multi class logistic regression gives an overall accuracy and precision of 98.4%. Recalling/remembering performance is 99%. Thus multi class logistic regression is the best model for prediction of flight delays in the COVID-19. The confusion matrix for the multi class logistic regression shows that 87.2% flights actually not delayed are predicted not delayed. The flights actually not delayed but wrongly predicted delayed are12.7%. The strength of relation with departure delay, carrier delay, late aircraft delay, weather delay and NAS delay, are 94%, 53%, 35%, 21%, and 14%, respectively. There is a weak negative relation (almost unrelated) with the air time and arrival delay. Security delay and arrival delay are also almost unrelated with strength of 1% relationship. Based on these diagnostic analytics, it is recommended as perspective to take due care reducing departure delay, carrier delay, Late aircraft delay, weather delay and Nas delay, respectively, considerably with effect of 94%, 53%, 35%, 21%, and 14% in disrupted situations. The proposed models have MAE of 2% for Neural Network Regression, Decision Forest Regression, Boosted Decision Tree Regression, respectively, and, RMSE approximately, 11%, 12%, 11%, respectively.

Featured Image

Photo by Miguel Ángel Sanz on Unsplash

Photo by Miguel Ángel Sanz on Unsplash

Why is it important?

This study is important to predict the event of airline flight delays in the disrupted situations. Due to the outbreak of the COVID 19, many flights were delayed causing a major disruption in the flight traffic control. To predict the correct status of the flight is important information for an airline business and better passenger service in the pandemic situation. This knowledge can save time, money, energy and result in better passenger service. It is reported that artificial intelligence and machine learning techniques have tremendous prediction capabilities. Thus the problem of the proposed research problem is “How to exploit the prediction capabilities of the artificial intelligence and machine learning techniques for predicting flight delays in COVID-19 pandemic situations?”

Perspectives