In the present scenario of domestic flights in USA, there have been numerous instances of flight delays and cancellations. In the United States, the American Airlines, Inc. have been one of the most entrusted and the world's largest airline in terms of number of destinations served. But when it comes to domestic flights, AA has not lived up to the expectations in terms of punctuality or on-time performance. Flight Delays also result in airline companies operating commercial flights to incur huge losses. So, they are trying their best to prevent or avoid Flight Delays and Cancellations by taking certain measures. This study aims at analyzing flight information of US domestic flights operated by American Airlines, covering top 5 busiest airports of US and predicting possible arrival delay of the flight using Data Mining and Machine Learning Approaches. The Gradient Boosting Classifier Model is deployed by training and hyper-parameter tuning it, achieving a maximum accuracy of 85.73%. Such an Intelligent System is very essential in foretelling flights'on-time performance.
Horiguchi, Yuji (Kyoto University) | Baba, Yukino (Kyoto University) | Kashima, Hisashi (Kyoto University) | Suzuki, Masahito ( Peach Aviation Limited ) | Kayahara, Hiroki (Peach Aviation Limited) | Maeno, Jun (Peach Aviation Limited)
Low-cost airlines (LCAs) represent a new category of airlines that provides low-fare flights. The rise and growth of LCAs has intensified the price competition among airlines, and LCAs require continuous efforts to reduce their operating costs to lower flight prices; however, LCA passengers still demand high-quality services. A common measure of airline service quality is on-time departure performance. Be- cause LCAs apply efficient aircraft utilization and the time between flights is likely to be small, additional effort is required to avoid flight delays and improve their service quality. In this paper, we apply state-of-the-art predictive modeling approaches to real airline datasets and investigate the feasibility of machine learning methods for cost reduction and service quality improvement in LCAs. We address two prediction problems: fuel consumption prediction and flight delay prediction. We train predictive models using flight and passenger information, and our experiment results show that our regression model predicts the amount of fuel consumption more accurately than flight dispatchers, and our binary classifier achieves an area under the ROC curve (AUC) of 0.75 for predicting a delay of a specific flight route.
The investigation described in this paper is situated within the context of the United States Air Traffic Management (ATM) System. The study included eight dyads engaged in a specific collaborative problem-solving task focusing on inefficiencies in the ATM system. The investigation focuses on how problem solving proceeds when the team members are from two distinct yet interdependent organizations with unique knowledge and expertise, are spatially distributed, have a shared display available to them, and must communicate by telephone rather than face to face. The findings reported here include results of an analysis of the verbal interaction behavior of each dyad with particular focus on the proposal of solutions to the problem task and the sharing of uniquely held knowledge that was necessary to create an environment of shared understanding between the dyad partners.
Air passengers leaving Gatwick have suffered the longest average delays during summer getaways from major UK airports, BBC analysis reveals. Those travelling to and from the UK on EasyJet flights have waited the longest among the 10 busiest airlines. Figures collected by the Civil Aviation Authority (CAA) during the last two summers reveal the typical delays. All flyers using EasyJet had an average delay of 24 minutes, and those leaving from Gatwick waited 27 minutes. Both said they appeared at the top of the delay list partly as a result of having among the biggest number of flights.