Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach
Trajectory prediction is an essential component of the next generation national air
transportation system. Reliable trajectory prediction models need to consider uncertainties …
transportation system. Reliable trajectory prediction models need to consider uncertainties …
Air traffic density prediction using Bayesian ensemble graph attention network (BEGAN)
Air traffic density prediction is crucial to aviation safety and air traffic management (ATM).
Understanding the complex spatial–temporal varying traffic patterns and the inter …
Understanding the complex spatial–temporal varying traffic patterns and the inter …
Bayesian spatio-temporal graph transformer network (b-star) for multi-aircraft trajectory prediction
Abstract Multi-Agent Trajectory Prediction is a critical and challenging component across
different safety–critical engineering applications, eg, autonomous driving and flight systems …
different safety–critical engineering applications, eg, autonomous driving and flight systems …
Conditional generative adversarial networks (CGAN) for aircraft trajectory prediction considering weather effects
The development of air traffic trajectory prediction models is a key objective of the next
generation (NextGen) national air transportation system. Significant uncertainties associated …
generation (NextGen) national air transportation system. Significant uncertainties associated …
A recurrent neural network approach for aircraft trajectory prediction with weather features from sherlock
The design of future US air traffic system, referred to as NextGen [1], is the subject of current
research at universities and research centers around the country [2]. The developing of …
research at universities and research centers around the country [2]. The developing of …
[PDF][PDF] Aircraft trajectory prediction using LSTM neural network with embedded convolutional layer
The development of convective weather avoidance algorithm is crucial for aviation
operations and it is also a key objective of the next generation air traffic management …
operations and it is also a key objective of the next generation air traffic management …
Posterior Regularized Bayesian Neural Network incorporating soft and hard knowledge constraints
Abstract Neural Networks (NNs) have been widely used in supervised learning due to their
ability to model complex nonlinear patterns, often presented in high-dimensional data such …
ability to model complex nonlinear patterns, often presented in high-dimensional data such …
PIGAT: Physics-Informed Graph Attention Transformer for Air Traffic State Prediction
Efficient and resilient traffic management relies on accurate prediction of air traffic states.
However, the complex spatial-temporal dependencies of air traffic networks make this task …
However, the complex spatial-temporal dependencies of air traffic networks make this task …
Probabilistic aircraft trajectory prediction considering weather uncertainties using dropout as Bayesian approximate variational inference
In the context of air traffic management (ATM), an accurate and reliable prediction of the
aircraft's trajectory is of critical importance. The enhanced predictability can decrease the …
aircraft's trajectory is of critical importance. The enhanced predictability can decrease the …
Dynamic airspace sectorization with machine learning enhanced workload prediction and clustering
Addressing the complexities of modern Air Traffic Management (ATM), this paper introduces
a novel framework for dynamic airspace sectorization, tailored to enhance efficiency and …
a novel framework for dynamic airspace sectorization, tailored to enhance efficiency and …