Predicting Operational Disruption Probabilities for Arrival Planning and Flow Management

The Operational Disruption Forecasting System (ODFS) predicts the uncertainties in the arrival process by integrating a suite of deep learning models with live data streams and streaming analytics to forecast the possible evolution of arrivals into the terminal airspace over the next 15 hours. The innovation accomplishes that objective by leveraging knowledge of the airspace structure and procedures, assessment of weather forecast accuracy, predictions of FAA-imposed constraints, and airport capacity forecasts to present a complete assessment of the future evolutionary paths of the terminal airspace.Beginning 15 hours ahead, ODFS generates probabilistic airport capacity scenarios based on weather forecasts, arrival and departure demand, and predictive models of weather forecast uncertainty. As the forecast window shortens, more information is incorporated into the prediction system. Predictions of possible convective weather impacts on arrival flows and runway capacity, and the potential for FAA-imposed traffic management initiatives (TMI), add more potential for disruption to planned operations. Enroute and terminal convective weather and changes to airline schedules may create congestion at arrival fixes. A trajectory prediction model integrates those data and forecasts, generating higher fidelity predictions of aircraft ETAs to the runways.The Robust Analytics solution develops a multi-stage analytical process that generates probabilistic scenarios at each stage. Once integrated, the scenarios form a comprehensive picture of the uncertainties along the entire arrival process. Over time, higher fidelity datasets continuously update the predictions allowing dispatchers and controllers to adjust. The core modules of ODFS are airport capacity forecasts and a trajectory prediction model. Potential impacts of other constraints ‒ convective weather forecasts, TMI predictions, and arrival fix demand nbsp;‒ serve as inputs to the core models.