Forecasting flight delays using clustered models based on airport networks

Abstract

Estimating flight delays is important for airlines, airports, and passengers, as the delays are among major costs in air transportation. Each delay may cause a further propagation of delays. Hence, the delay pattern of an airport and the location of the airport in the network can provide useful information for other airports. We address the problem of forecasting flight delays of an airport, utilizing the network information as well as the delay patterns of similar airports in the network. The proposed “Clustered Airport Modeling” (CAM) approach builds a representative time-series for each group of airports and fits a common model (e.g., REG-ARIMA) for each, using the network based features as regressors. The models are then applied individually to each airport data for predicting the airport’s flight delays. We also performed a network based analysis of the airports and identified the Betweenness Centrality (BC) score as an effective feature in forecasting the flight delays. The experiments on flight data over seven years using 305 US airports show that CAM provides accurate forecasts of flight delays.