Our innovation offers NASA and the aviation community a robust, extensible data processing and analysis infrastructure that supports predicting the near-future status of terminal airspace to aid decision-making. The Robust Analysis for Predictive In-time Decision Support (RAPIDS) will generate short-term horizon forecasts (10-20 minutes) for the state of the airspace. RAPIDS combines known procedural structures of the airspace with historical operational data to predict future aircraft trajectories in the terminal airspace. The predicted trajectories will then be used to identify operational/safety stress in the airspace and support mitigation actions. Most of the prevalent research and tools for predictive safety monitoring focus on reactive or tactical predictions (several seconds to minutes ahead). As the forecast horizon increases, the uncertainty in the projected states increases. RAPIDS aims to reduce the uncertainty in the tactical and strategic levels by integrating machine learning algorithms trained on historic operations. Commercial aircraft operational patterns are repetitive due to the highly structured operational procedures used in terminal airspace, thus allowing machine learning models to extract the underlying operational patterns.
RAPIDS contributes to NASA’s aviation safety objectives by providing a framework for generating predictions for terminal airspace states, thus enabling in-time actionable advisories to stakeholders. RAPIDS supports IASMS goals by providing reliable trajectory predictions, which are crucial to effective predictions of the terminal airspace states. The trajectory predictions become relevant for in-time prognostics, allowing the stakeholders to respond with mitigating actions that are key to IASMS.
RAPIDS has commercial aviation benefits by giving operators an easy to deploy prediction tool to identify operational risks in-time before entering the terminal airspace of an airport. The trajectory predictions will provide sufficient lead-time for the commercial aviation stakeholders to react more optimally to operational challenges thus promising more efficient operations.