This study analyzes high-dimensional data from Beijing International Airport and presents a
practical flight delay prediction model. Following a multifactor approach, a novel deep belief …

Flight delay prediction has become one of the most critical topics in intelligent aviation
systems due to its essential role in flight scheduling, airline planning, and airport operation …

In this paper, we propose a novel prediction framework (ST-Random Forest) for flight delay
prediction from temporal and spatial perspective. We first apply complex network theory to …

Accurate flight delay prediction is fundamental to establish the more efficient airline
business. Recent studies have been focused on applying machine learning methods to …

To model and forecast flight delays accurately, it is crucial to harness various vehicle
trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor …

Flight delay prediction has recently gained growing popularity due to the significant role it
plays in efficient airline and airport operation. Most of the previous prediction works consider …

The primary objective of this study is to predict network-wide multi-step-ahead flight delay. A
novel graph-to-sequence learning architecture with attention mechanism (AG2S-Net) is …

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 …

Flight delay is inevitable and it plays an important role in both profits and loss of the airlines.
An accurate estimation of flight delay is critical for airlines because the results can be …

Deep learning has achieved significant improvement in various machine learning tasks
including image recognition, speech recognition, machine translation and etc. Inspired by …