相关论文: A Data Mining Approach to Flight Arrival Delay Pre…

This article presents a model for the economic value of extra capacity at an airport. The model is based on a series of functional relationships linking the benefits of extra capacity and the associated costs. It takes into account the cost…

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…

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.…

On-time performance is a critical challenge in the airline industry, leading to large operational and customer dissatisfaction costs. The tail assignment problem builds the sequences of flights or routes followed by individual airplanes.…

In this work we compare the performance of several machine learning algorithms applied to the problem of modelling air transport demand. Forecasting in the air transport industry is an essential part of planning and managing because of the…

Flight delay prediction has become a key focus in air traffic management (ATM), as delays reflect inefficiencies in the system. This paper proposes LLM4Delay, a large language model (LLM)-based framework for predicting flight delays from…

Ground staff agents of airlines operate many jobs at airports such as passengers check-in, planes cleaning, etc. Shift planning aims at building the sequences of jobs operated by ground staff agents, and have been widely studied given its…

Commercial aviation is one of the biggest contributors towards climate change. We propose to reduce environmental impact of aviation by considering solutions that would reduce the flight time. Specifically, we first consider improving winds…

Aircraft landing time (ALT) prediction is crucial for air traffic management, especially for arrival aircraft sequencing on the runway. In this study, a trajectory image-based deep learning method is proposed to predict ALTs for the…

Accurate flight delay prediction is crucial for the secure and effective operation of the air traffic system. Recent advances in modeling inter-airport relationships present a promising approach for investigating flight delay prediction…

Data science, and machine learning in particular, is rapidly transforming the scientific and industrial landscapes. The aerospace industry is poised to capitalize on big data and machine learning, which excels at solving the types of…

The impact of air transport delays and their propagation has long been studied, mainly from environmental and mobility viewpoints, using a wide range of data analysis tools and simulations. Less attention has nevertheless been devoted to…

The Aircraft Landing Problem (ALP) is one of the challenging problems in aircraft transportation and management. The challenge is to schedule the arriving aircraft in a sequence so that the cost and delays are optimized. There are various…

Decision makers often need to rely on imperfect probabilistic forecasts. While average performance metrics are typically available, it is difficult to assess the quality of individual forecasts and the corresponding utilities. To convey…

Revenue management can enable airline corporations to maximize the revenue generated from each scheduled flight departing in their transportation network by means of finding the optimal policies for differential pricing, seat inventory…

We propose AI-CARGO, a revenue management system for air-cargo that combines machine learning prediction with decision-making using mathematical optimization methods. AI-CARGO addresses a problem that is unique to the air-cargo business,…

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and…

Due to the stochastic nature of departure operations, working at full capacity makes major US airports very sensitive to uncertainties. Consequently, airport ground operations face critically congested taxiways and long runway queues. In…

Predicting air traffic congestion and flow management is essential for airlines and Air Navigation Service Providers (ANSP) to enhance operational efficiency. Accurate estimates of future airport capacity and airspace density are vital for…

In this paper, the scheduling problems of landing and takeoff aircraft on a same runway and on dual runways are addressed. In contrast to the approaches based on mixed-integer optimization models in existing works, our approach focuses on…