Related papers: Exploratory Data Analysis for Airline Disruption M…

Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cause massive economic losses, affect airlines' reputations,…

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 disruption management traditionally seeks to address three problem dimensions: aircraft scheduling, crew scheduling, and passenger scheduling, in that order. However, current efforts have, at most, only addressed the first two…

Limiting flight delays during operations has become a critical research topic in recent years due to their prohibitive impact on airlines, airports, and passengers. A popular strategy for addressing this problem considers the uncertainty of…

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…

Airline operations are prone to delays and disruptions, since the schedules are generally tight and depend on a lot of resources. When disruptions occur, the flight schedule needs to be adjusted such that the operation can continue. Since…

The aviation industry has experienced constant growth in air traffic since the deregulation of the U.S. airline industry in 1978. As a result, flight delays have become a major concern for airlines and passengers, leading to significant…

This research investigates flight delay trends by examining factors such as departure time, airline, and airport. It employs regression machine learning methods to predict the contributions of various sources to delays. Time-series models,…

Airlines are critical today for carrying people and commodities on time. Any delay in the schedule of these planes can potentially disrupt the business and trade of thousands of employees at any given time. Therefore, precise flight delay…

We investigate the factors contributing to departure and arrival delays at a major international airport and develop predictive models to estimate both the likelihood and duration of delays. Using logistic regression, random forest, and…

This paper presents AIRS, a day-of-operations disruption-recovery system. AIRS.ACR models integrated aircraft-crew recovery on a Time-Space Network (TSN) and solves a mixed-integer linear program (MILP) that enforces rotation continuity,…

In practice, both passenger and cargo flights are vulnerable to unexpected factors, such as adverse weather, airport flow control, crew absence, unexpected aircraft maintenance, and pandemic, which can cause disruptions in flight schedules.…

Technologically driven transport systems are characterized by a networked structure connecting operation centers and by a dynamics ruled by pre-established schedules. Schedules impose serious constraints on the timing of the operations,…

Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption…

Predicting if passengers in a connecting flight will lose their connection is paramount for airline profitability. We present novel machine learning-based decision support models for the different stages of connection flight management,…

This study explores the approaches used by airlines in setting flight times. It highlights the need to balance operational and strategic factors, such as optimizing the use of resources - including aircraft, crew, and fuel - and managing…

The prediction of flight delays plays a significantly important role for airlines and travelers because flight delays cause not only tremendous economic loss but also potential security risks. In this work, we aim to integrate multiple data…

The severity of natural disasters is increasing every year, impacting many people's lives. During the response phase of disasters, airports are important hubs where relief aid arrives and people need to be evacuated. However, the airport…

Disruptions in the National Airspace System (NAS) lead to significant losses to air traffic system participants and raise public concerns. We apply two methods, cluster analysis and anomaly detection models, to identify operational…

Flight delays hurt airlines, airports, and passengers. Their prediction is crucial during the decision-making process for all players of commercial aviation. Moreover, the development of accurate prediction models for flight delays became…