Autonomous Forecast Trend Monitoring in Support of Air Traffic Management Efficacy Improvements

Abstract

Our research effort seeks to design and implement an autonomous toolchain which will run in the background throughout the day of operations and monitor active or pending Traffic Management Initiatives in the National Airspace System, looking for potential opportunities to reduce their scope or duration. The toolchain is comprised of three modules: the Forecast Trend Analysis module, the cloud-based Fast-Time Simulation Engine for rapid evaluation of potential weather impact and air traffic management scenarios, and the Traffic Management Initiative Action Analysis module which generates alerts when credible weather impact reduction opportunities are identified. This paper focuses mostly on the first of these three components, the Autonomous Forecast Trend Analysis module. The system being developed translates both convective and non-convective weather forecasts into corresponding airspace and airport capacity reductions and weighs them against forecasted traffic demand and active traffic management initiatives—to assess, and potentially reduce, their operational impact.