A novel time-series data analytics approach can improve the accuracy of real-time forecasts of the time-line and magnitude for irregular operations for airlines and for Air Traffic Flow Management (ATFM). Contemporary methods used by airlines for implementing irregular operations “playbook” exhibit forecasting of these events that frequently over/under estimate the timing and magnitude of these events (i.e. too late to be effective, or implemented when it was not required.) This results in excess costs and lost efficiency.
Further, the ability to forecast the timing and magnitude of irregular operations will be critical for the implementation of end-to-end Trajectory-based Operations particularly in an environment in which airline “business rules” and tactical preferences are shared by the airlines with ATFM to improve the efficiency of operations.
With improved forecasting of irregular operations, airlines and ATFM can address demand-capacity imbalances in the presence of uncertainty at critical choke-points in the NAS.
The project will extend research conducted on times-series predictive analytics for real-time forecasting the presence, the time-line, and the magnitude of irregular operations in the NAS and for individual airline networks. The approach that has yielded success is to use a novel Machine Learning framework that uses recursive forecasting Bayesian updating. The recursive predictions and the Bayesian update provide a significant boost to the accuracy of neural networks and other Machine Learning algorithms. This project will develop and demonstrate the novel approach different airline networks and the National Airspace System (NAS).
This novel approach for accurately forecasting irregular operations addresses NASA milestones for Increasing Diverse Operations (IDO) and for achieving the benefits of con-ops for high density, complex airspace such as the Northeast Corridor as well as other regions in the NAS. This capability will be critical for the implementation of end-to-end Trajectory-based Operations particularly in an environment in which airline tactical preferences are shared by the airlines with ATFM to improve the efficiency of operations.
This novel approach for accurately forecasting irregular operations provides the technology for forcecasting for disrupted operations in complex network-of-networks. These methods are applicable to UTM for sUAS and UAM. These methods are applicable to surface transportation, energy and water systems, and supply chains.