An Operations-Structured Model for Strategic Prediction of Airport Arrival Rate and Departure Rate Futures

Abstract

Motivated by needs in strategic AirTraffic Flow Management, we propose a model for forecasting airport arrival and departure capacity over a full-day look-ahead horizon. The focus of the modeling effort is on a small set of high-congestion airports, for which we propose a multi-stage prediction model. In this article, a core piece of the model -an operations-driven prediction for airport runway configurations and baseline capacities in terms of weather and operational regressor -is developed. We demonstrate the model's performance through case studies for two high-congestion airports, Chicago O'Hare International Airport (KORD) and Boston Logan International Airport (KBOS).

Terminal Traffic Situation Prediction Model under the Influence of Weather Based on Deep Learning Approaches

“…The closer to 1, the better the variables of the equation explain y, and the better this model fits the data. In Equation (13), it is the summed average of the precision and recall rates, with a maximum of 1 and a minimum of 0.…”

“…Smith et al [11] used a support vector machine to predict the airport capacity under the influence of weather, and then used the prediction results to predict the airport delay [12]. Dhal et al [13] proposed a multi-level forecasting model that forecasts the arrival and departure capacity of high-congestion airports within a day for the weather.…”

Cyber- Threat Assessment for the Air Traffic Management System: A Network Controls Approach

“…The convective coverage predicted by the model can then be translated to a reduction in the en route capacity. Meanwhile, airport capacity trajectories can be obtained from local wind, ceiling, and convection variables obtained from ensemble forecasts (or derived influence models), or alternately from terminal aerodrome forecasts (e.g., [35][36][37]). The weather layer of the proposed model includes the spatiotemporal models for weather evolution, and their translation to airport and en route capacities, see Figure 1b.…”