Analysis of short-term forecasting for flight arrival time

• Spline smoothing techniques are applied to the forecasting after a flight departs from an airport.
• Flights arriving at Denver International Airport from various U.S. cities are used.
• Departure delay time is the most important factor for improving prediction.
• The skew t distibution is utilized to compute the probability of flight arrival time.

We suggest various methodologies to provide short-term forecasting of flight arrival times. Flights arriving at Denver International Airport from various U.S. cities during 2010 are used for the model estimation, and the forecasting is applied to 2011 flights. Forecasting proceeds from the time at which a flight departs from an airport. Prediction models using the spline smoothing-based nonparametric additive techniques are applied and compared with benchmarks. We also provide a method for computing the probability of flight arrival time by fitting the skew t distribution to the models’ residuals. Our empirical results indicate that a nonparametric additive model dominantly outperforms the other models considered. In terms of effect of predictor variables, departure delay time, scheduled airborne time, airlines, and weather conditions significantly improve forecasting accuracy, along with seasonal variables. In particular, departure delay time is the most important factor for substantially improving prediction performance.