A Network-Based Model for Predicting Air Traffic Delays

This paper presents a new model for predicting delays in the National Airspace System (NAS). The proposed model uses Random Forest (RF) algorithms, considering both temporal and spatial (that is, network) delay states as explanatory variables. In addition to local delay variables that describe the arrival or departure delay states of the most influential airports and origin-destination (OD) pairs in the network, we propose new network delay variables that depict the global delay state of the entire NAS at the time of prediction. The paper analyzes both the classification and regression performance of the proposed prediction models, which are trained and validated on 2007 and 2008 ASPM data. The predictive performance of the model is evaluated using the 100 most delayed OD pairs in the NAS: the results show that given a 2-hour prediction horizon, the average test error across these 100 OD pairs is 19% when classifying delays as above or below 60 min. The effect of changes in the classification threshold and prediction horizon on model performance are also studied.