7 research outputs found
A METHODOLOGY FOR THE PREDICTION AND ANALYSIS OF PRECURSORS TO FLIGHT ADVERSE EVENTS
Get PDFAir transportation is known to be the safest mean of transportation nowadays. The drastic improvements in aviation safety since its gain in popularity are undeniably a factor in the industry's growth over the last several decades. This growth brought social and economic benefits throughout the world and was expected to keep its momentum pre-COVID-19. Stakeholders such as the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), the National Transportation Safety Board (NTSB), aircraft manufactures, and airlines have developed systems, techniques, and technologies that are to thank for today's overall safety improvements and the reduction of accidents. The industry's maintained growth is welcomed, but current safety performances have been observed to stagnate instead of declining. With safety initiatives such as the Flight Operational Quality Assurance (FOQA) program and the growing number of aviation data, many of the previous techniques used to understand the causes of accidents are not scalable. These reasons led to the development of novel methods leveraging advanced analytical tools such as machine learning and deep learning. However, current use cases have focused mainly on anomaly detection and system health monitoring, which does not bring enough reaction time to deal with an imminent event.
This research proposes the improvement of aviation safety through precursor mining. Precursors are defined as events that are highly correlated to the adverse event that they precede. Therefore, they provide predictive capabilities and can be used to explain pre-defined events. This thesis uses publicly available flight data to 1) develop a novel deep learning method to identify and rank precursors of multiple adverse events, 2) use unsupervised learning algorithms to group flights based on their precursors to identify potential causes for these events at a fleet-level, and finally 3) detect novelty to ensure that the developed precursor models operate within their limits and that new non pre-defined adverse events could be detected.M.S
Application of Machine Learning to the Analysis and Prediction of the Coincidence of Ground Delay Programs and Ground Stop
Get PDFTraffic Management Initiatives such as Ground Delay Programs and Ground Stops are
implemented by traffic management personnel to control air traffic volume to constrained
airports when traffic demand is projected to exceed the airports’ acceptance rate due to
conditions such as inclement weather, volume constraints, etc. Ground Delay Programs are
issued for lengthy periods of time and aircraft are assigned departure times later than scheduled.
Ground Stops on the other hand, are issued for short periods of time and aircraft are not
permitted to land at the constrained airport. Occasionally, Ground Stops are issued during
an ongoing Ground Delay Program, and vice versa, which hinders the efficient planning and
implementation of these Traffic Management Initiatives. This research proposes a methodology
to help stakeholders better capture the impact of the coincidence of weather related Ground
Delay Programs and Ground Stops, and potentially help reduce the number and duration of
such coincidences. This is achieved by leveraging Machine Learning techniques to predict their
coincidence at a given hour, predict which Traffic Management Initiative would precede the
other during their coincidence, and identify key predictors that cause their coincidence. The
Random Forests Machine Learning algorithm was identified as the best suited algorithm for
predicting the coincidence of weather-related Ground Delay Programs and Ground Stops, as
well as the Traffic Management Initiative that would precede the other during their coincidence
