Airport delay prediction using weather-impacted traffic index (WITI) model

In this paper, we present a new predictive model for estimating airport delay using data from weather forecast products. We use the well established Weather Impacted Traffic Index (WITI) toolset and metric. The latter quantifies the “front-end” impact of weather and traffic demand on the NAS and is well correlated with NAS delays, which makes WITI a reasonably good high-level model of NAS performance. WITI-FA (“Forecast Accuracy”) is the forecast-weather counterpart to WITI: it can use various convective forecast products, as well as Terminal Area Forecast (TAF), and quantify forecast weather impact on the NAS. We show how these models can be refined and re-oriented toward predictive capability. First, instead of using just three WITI components, we break down the weather impacts by type, e.g. wind, snow, low ceilings, en-route thunderstorms, volume, etc - twelve components in total. Second, instead of using a NAS-wide WITI model, we “zoom in” on individual airports. The model is calibrated to minutes of delay for a given airport on an hourly basis. Having trained the model using historical airport performance and actual weather / scheduled traffic data, we then apply it in a predictive mode. The paper contains multiple examples and comparisons of predicted vs. actual delays at major airports under various weather conditions. In addition to predicting delays, the model can be used as a decision support tool. If predicted delays are too high, WITI can be run in what-if mode to gauge demand reduction, guaranteeing sustainable delays in adverse weather conditions. This could also be helpful to airlines when they need to decide on the amount of flight cancellations. Lastly, our airport delay predictor model can be used to compare the efficacy of different weather forecast products