A Design Model For Predicting Flight Delays By Identifying Data Patterns Through Big Data And Quantum Machine Learning

Abstract

The rapid growth of the global economy has resulted in a considerable increase in demand for air travel. One of the hardest issues in the aviation industry has indeed been highlighted to be flight delays. Customers find flight delays inconvenient, but they also expense airlines money. If a flight delay occurs customer faced to disturbing his schedule and loss of time and money. Frequently occurring flight delay make an impact on huge economic loss. For the airport, flight delay disturbs normal operations of airport. Due to the expanding role that the aviation industry plays in the current global transportation sector, several businesses rely on a variety of different airlines to connect them with other parts of the world. There are many reasons flight delay occurs. This delay can be a type of arrival delay or departure delay. Due to bad weather, late arrival of aircraft, mechanical reason, fuel, security delay, air delay etc. are main reasons of flight delay. So, the flight delay prediction is a challenging task nowadays. For airlines, predicting flight delays accurately is essential since the information can be used to enhance customer service and agency income. The solution to this issue is accurate prediction of these flight delays, which allows travellers to be well prepared for the disruption to their travel plans and allows airlines to address potential causes of the flight delays in advance in order to lessen the negative effects. Research is still going on flight delay prediction. There are many methods are exists using machine learning and deep learning for flight delay prediction. Many Researchers used algorithms like Decision tree, Logistic regression, Neural network, Multiple linear Regression, SVM LightGBM , extremely randomized trees, Deep Learning (DL), Levenberg-Marquart algorithm etc. for flight delay prediction. All these algorithms are applied to the United State Transportation bureau dataset which is available freely. In this research work flight delay prediction is proposed with Quantu