Deep Spatio-Temporal Neural Networks for Risk Prediction and Decision Support in Aviation Operations

Abstract

The maintenance and improvement of safety are among the most critical concerns in civil aviation operations. Due to the increased availability of data and improvements in computing power, applying artificial intelligence technologies to reduce risk in aviation safety has gained momentum. In this paper, a framework is developed to build a predictive model of future aircraft trajectory that can be utilized online to assist air crews in their decision-making during approach. Flight data parameters from the approach phase between certain approach altitudes (also called gates) are utilized for training an offline model that predicts the aircraft’s ground speed at future points. This model is developed by combining convolutional neural networks (CNNs) and long short-term memory (LSTM) layers. Due to the myriad of model combinations possible, hyperband algorithm is used to automate the hyperparameter tuning process to choose the best possible model. The validated offline model can then be used to predict the aircraft’s future states and provide decision-support to air crews. The method is demonstrated using publicly available Flight Operations Quality Assurance (FOQA) data from the National Aeronautics and Space Administration (NASA). The developed model can predict the ground speed at an accuracy between 1.27% and 2.69% relative root-mean-square error. A safety score is also evaluated considering the upper and lower bounds of variation observed within the available data set. Thus, the developed model represents an improved performance over existing techniques in literature and shows significant promise for decision-support in aviation operations.