Modeling

Experiencing high delays is a “bad day” for the National Airspace System (NAS). We apply machine learning algorithms to model the system delay and predict high delay days in the NAS for the 2010s. A broader scope of factors that may affect the system delay is examined, including queueing delays, terminal conditions, en route weather, wind, traffic volume, and special events. We train models to relate the system delay to these features spatially and temporally, and compare the performance of penalized regressions, kernelized support vector regressions, and ensemble regressions. The learned...

We consider the cost of air cargo flight delay related to late package deliveries, which has received scant attention in previous research. We developed two models to estimate the costs of flight delay. First, we estimate a mixed-logit model to investigate the factors that influence late deliveries, with specific emphasis on flight on-time performance. Then we build a regression model to monetize the loss of late deliveries, using the hedonic approach to estimate the degradation in product value resulting from less reliable on-time package delivery. Estimates of flight delay costs for four...

In this paper, we employ the long-short-term memory model (LSTM) to predict the real-time go-around probability as an arrival flight is approaching JFK airport and within 10 nm of the landing runway threshold. We further develop methods to examine the causes to go-around occurrences both from a global view and an individual flight perspective. According to our results, in-trail spacing, and simultaneous runway operation appear to be the top factors that contribute to overall go-around occurrences. We then integrate these pre-trained models and analyses with real-time data streaming, and...

In this paper, we present a dynamic stochastic integer programming (IP) model for the single airport ground holding problem, in which ground delays assigned to flights can be revised during different decision stages, based on weather forecasts. The performance gain from our model is particularly significant in the following cases: (1) under stringent ground holding policy, (2) when an early ground delay program (GDP) cancellation is likely, and (3) for airports where the ratio between adverse and fair weather capacities is lower. The choice of ground delay cost component in the objective...