Prediction and Analysis of Ground Stops with Machine Learning

Date
2020-01Author
Mangortey, Eugene
Puranik, Tejas G.
Pinon, Olivia J.
Mavris, Dimitri N.
Metadata
Show full item recordAbstract
A flight is considered to be delayed when it arrives 15 or more minutes later than scheduled.
Delays attributed to the National Airspace System are one of the most common type of delays.
Such delays may be caused by Traffic Management Initiatives (TMI) such as Ground Stops
(GS), issued at affected airports. Ground Stops are implemented to control air traffic volume
to specific airports where the projected traffic demand is expected to exceed the airports’
acceptance rate over a short period of time due to conditions such as inclement weather, volume
constraints, closed runways, etc. Ground Stops can be considered to be the strictest Traffic
Management Initiative (TMI), particularly because all flights destined to affected airports are
grounded until conditions improve. Efforts have been made over the years to reduce the impact
of Traffic Management Initiatives on airports and flight operations. However, these efforts have
largely focused on otherTraffic Management Initiatives such as Ground Delay Programs (GDP),
due to their frequency and duration compared to Ground Stops. Limited work has also been
carried out on Ground Stops because of the limited amount of time that traffic management
personnel often have between planning and implementing Ground Stops and external factors
that influence decisions of traffic management personnel. Consequently, this research primarily
focuses on the prediction of weather-related Ground Stops at Newark Liberty International
(EWR) and LaGuardia (LGA) airports, with the secondary goal of gaining insights into factors
that influence their occurrence. It is expected that this research will provide stakeholders with
further insights into factors that influence the occurrence of weather-related Ground Stops at
both airports. This is achieved by benchmarking Machine Learning algorithms in order to
identify the best suited algorithm(s) for the prediction models, and identifying and analyzing key
factors that influence the occurrence of weather-related Ground Stops at both airports. This is
achieved by 1) fusing data from the Traffic Flow Management System (TFMS) and Automated
Surface Observing Systems (ASOS) datasets, and 2) leveraging supervised Machine Learning
algorithms to predict the occurrence of weather-related Ground Stops. The performance of
these algorithms is evaluated using balanced accuracy, and identifies the Boosting Ensemble
algorithm as the best suited algorithm for predicting the occurrence of Ground Stops at EWR
and LGA. Further analysis also revealed that model performance is significantly better when
using balanced datasets compared to imbalanced datasets.