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.
The Big Data analytics are a logical analysis of very large scale datasets. The data analysis enhances an organization and improve the decision making process. In this article, we present Airline Delay Analysis and Prediction to analyze airline datasets with the combination of weather dataset. In this research work, we consider various attributes to analyze flight delay, for example, day-wise, airline-wise, cloud cover, temperature, etc. Moreover, we present rigorous experiments on various machine learning model to predict correctly the delay of a flight, namely, logistic regression with L2 regularization, Gaussian Naive Bayes, K-Nearest Neighbors, Decision Tree classifier and Random forest model. The accuracy of the Random Forest model is 82% with a delay threshold of 15 minutes of flight delay. The analysis is carried out using dataset from 1987 to 2008, the training is conducted with dataset from 2000 to 2007 and validated prediction result using 2008 data. Moreover, we have got recall 99% in the Random Forest model.
In this article, we report on the efficiency and effectiveness of multiagent reinforcement learning methods (MARL) for the computation of flight delays to resolve congestion problems in the Air Traffic Management (ATM) domain. Specifically, we aim to resolve cases where demand of airspace use exceeds capacity (demand-capacity problems), via imposing ground delays to flights at the pre-tactical stage of operations (i.e. few days to few hours before operation). Casting this into the multiagent domain, agents, representing flights, need to decide on own delays w.r.t. own preferences, having no information about others' payoffs, preferences and constraints, while they plan to execute their trajectories jointly with others, adhering to operational constraints. Specifically, we formalize the problem as a multiagent Markov Decision Process (MA-MDP) and we show that it can be considered as a Markov game in which interacting agents need to reach an equilibrium: What makes the problem more interesting is the dynamic setting in which agents operate, which is also due to the unforeseen, emergent effects of their decisions in the whole system. We propose collaborative multiagent reinforcement learning methods to resolve demand-capacity imbalances: Extensive experimental study on real-world cases, shows the potential of the proposed approaches in resolving problems, while advanced visualizations provide detailed views towards understanding the quality of solutions provided.