We propose to design a data-driven decision support tool that quantifies and predicts the impacts of Playbook reroutes at the strategic level. This will improve the efficiency and throughput of the NAS. Our machine learning model will predict the net effects of US national reroutes in response to severe weather. This will enable faster, more accurate, and longer horizon predictions than traditional methods. Our model will form the core of a what-if analysis tool to enable air traffic managers to rank the Playbook reroute options by suitability and then understand the intended and unintended consequences of issuing such a reroute. The tool enables air traffic managers to take a strategic data-driven approach to choosing reroutes, thereby reducing disruptions to the national airspace. Currently, traffic managers lack data-driven analysis tools and instead rely on years of experience and personal preferences. Our team includes experts in traffic management, machine learning, and airspace data processing. Potential NASA Applications (Limit 1500 characters, approximately 150 words): Advances NASA ATM research to improve the efficiency and throughput of the NAS. Identifies new strategies for assessing quality of Playbook reroutes. Integration with NASAâs Digital Information Platform (DIP) enables other analytic service providers and Trajectory Based Operations (TBO) algorithms to reason over our predictions. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words): ANSP personnel will identify the net effects of traffic flows in response to proposed reroutes, severe weather, and subsequent reactions by local facilities, airlines, and pilots. Airlines will anticipate competitor behaviors in response to reroutes and can adjust their operations accordingly. Duration:
