Improving flight planning efficiency through machine learning

Abstract

Flight planning in support of multi-domain operations is a complex and manpower intensive process. Multiple factors such as changing environmental conditions, navigational data and constraints, air-traffic procedures and policies, maturation of routes and coordination with all stakeholders contribute to this complexity. These changing constraints result in multiple updates and deviations to the flight plans which eventually impact mission objectives and timing. This work applies state-of-the-art machine learning technologies to automatically determine the quality of generated flight plans to enable rapid verification and approval for plan filing. It also identifies preferred routes that are input into planner that results in higher flight plan acceptance rates. We leverage voluminous sources of real operational flight planning data and filed planning data to develop the models. Supervised learning is leveraged to predict whether a generated plan will be filed or rejected. The unsupervised learning component identifies flight plan preferences for feedback to the planner system. These models could be eventually deployed in a flight planning operational environment to reduce human effort, cost, and time to generate flyable plans. The results from this work could also be used to improve planner rules and search algorithms.