Data-Driven Approach using Machine Learning for Real-Time Flight Path Optimization

Airlines typically gather all available weather information before departure to generate flight routes that avoid hazardous weather while minimizing operating expenditures. However, pilots potentially have to perform in-flight re-planning as weather information can significantly change after original flight plans are created. One potential issue is that current in-flight re-planning systems are not fully automated; thus, pilots today perform some portions of the in-flight activities manually. Another potential issue is that weather forecasts used for the systems are not always accessible in a timely manner. This research attempts to resolve the potential issues by developing a framework that automatically performs in-flight re-planning continuously with the latest weather information sets available. This study specifically develops 1) a supervised machine learning-based wind prediction model to obtain continuous wind information, 2) an unsupervised machine learning-based short-term (i.e., every 10 minutes) convective weather prediction model to define reliable and up-to-date areas of convective weather, and 3) a designated points-based flight path optimization model that combines the A* search algorithm with a free-flight approach to find an optimal flight path. As a part of this research, statistical analyses are performed using real flights to prove the potential benefits and applicability of the proposed methodology. The results indicate that the framework developed by this research generates flight routes that reduce flight time by up to two percent in most cases. The outcome of this research establishes not only an automated framework that continuously performs flight path optimization but also provides a basis for optimizing flight routes for all categories of airplanes.Ph.D