Description: A significant portion of the NextGen research is aimed at (i) developing ground-side automation systems to assist controllers in strategic planning operations such as scheduling flights, and (ii) developing tactical controller decision support tools to separate and space the traffic. Central to the success of these automation systems is the ability to predict the future trajectory of any aircraft in the National Airspace System (NAS). The research related to this area is referred to as Trajectory Prediction (TP) and sometimes Trajectory Synthesis. Notwithstanding past research, TP remains a very challenging exercise and the quest for improved TP accuracy continues. Any improvements in TP can benefit a wide array of NextGen concepts pursued by NASA. The objective of the current research is to seek a novel approach to TP specifically aimed at addressing some of the deficiencies of the past TP research. The approach involves: (i) machine learning algorithms, and (ii) big data computational platforms. Phase I research will demonstrate the benefits of supervised and unsupervised machine learning algorithms for TP. Phase II research seeks to develop real-time trajectory prediction algorithms that can be used for a wide variety of NASA NextGen concepts.

Benefits: Algorithms developed under the current research are expected to directly contribute towards NASA's NextGen air traffic management research, especially to the Separation Assurance (SA) research focus area.

The TP algorithms developed under this research are expected to be applicable all over the National Airspace System. These algorithms could be part of the En Route Automation Modernization (ERAM) currently being developed by Lockheed Martin for the FAA. During Phase II research, Optimal Synthesis Inc. seeks to identify transition mechanisms for implementing these algorithms in ERAM software system.