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Use of AI/ML methods in optimizing airspace operations.

The objective of this internship is to process and analyze the current aviation and weather data available for select US airports and evaluate the feasibility of AI/ML methods and approach for airport capacity (arrival rate, the number of arriving aircraft the airport can handle per hour) predictions. Better understanding of the airport arrival rate will help in better handling of ground operations such as ground delay or ground stop at an airport due to a weather event. The student will develop understanding of the aviation operational data, feature engineering on such dataset and use of AI/ML methods for aviation operations use cases. 

Desired skill set:

Programming experience
Desired programming language: Python, Matlab scripting, C++
Data Analysis and analytics, 
Data visualization tools and software experience

 

Tasks to be accomplished:

The student shall process and analyze flight and weather data.
The student will set up the required links to the databases and data repositories to his/her code. 
The student shall build required models and algorithms to predict airport capacity (rate) using the available data, train the model and make predictions using the test data set. 
Student shall identify gaps and any challenges with this approach and document the findings. 
Student will document the work and give a presentation to the project team. 

 

Project Deliverables:

Submit monthly reports and final presentation
Provide working software code 
NASA Technical report (external publication is a bonus) 
Final technical presentation to the branch 
Weekly meetings to discuss progress and updates.