Mitigating Risk in Commercial Aviation Operations

NASA’s newly developed software leverages flight operations data (e.g., SWIM Terminal Data Distribution System (STDDS) information), and with it, can predict aviation related risks, such as unstable approaches of flights. To do this, the software inputs the complex, multi-source STDDS data, and outputs novel prediction and outcome information. The software converts the relatively inaccessible SWIM data from its native format that is not data science friendly into a format easily readable by most programs. The converted, model friendly data are then input into machine learning algorithms to enable risk prediction capabilities. The backend software sends the machine learning algorithm results to the front end software to display the results in appropriate user interfaces. These user interfaces can be deployed on different platforms including mobile phones and desktop computers and efficiently update models based on changes in the data. To allow for visualization, the software uses a commercially available mapping API. The data are visualized in several different ways, including a heat map layer that shows the risk score, with higher risk in areas of higher flight density, a polyline layer, which shows flight paths, and markers that can indicate a flight’s location in real time, among other things. The related patent is now available to license. Please note that NASA does not manufacturer products itself for commercial sale. Researchers at the NASA Langley Research Center have invented a software based on machine learning algorithms that uses data from over 200 airports and over 400 individual systems to predict aviation related risks. One source of the National Airspace System (NAS)’s flight operations data is available from the System Wide Information Management (SWIM) program which are not adequately leveraged due to their relative inaccessibility and the lack of software to collate and interpret the information. The new NASA software can transform the data into a usable form, then support real-time dispatchers guiding aircraft approaches and departures through the developed machine learning models. This has the potential to improve safety within commercial aviation terminal area operations, which will be especially pertinent as the number of flights per day increases. This software may also be leveraged to manage autonomous flight activity.