Competitive Advantages
- Uses Deep Learning Framework
- Can predict runway configuration and AARs in real time for a metroplex
- Marketable to Federal Aviation Administration (FAA), multi-airport systems (MASs), and airports
Summary
USF Inventors have developed Deep Learning Framework that uses high-precision gridded weather forecast data by two novel contributions. One: it uses assembled gridded weather forecast for the terminal airspace instead of an isolated station-based terminal weather forecast that has been used in existing literature. Second: it proposes a multi-layer convolutional neural network to predict both runway configurations and AARs for different airports in a multi-airport system. This invention developed an end-to-end system to predict real-time runway configurations and AAR simultaneously for multi-airport system, where synchronized air traffic operations are presented.
Typical Airline Production Process
Desired Partnerships
- License
- Sponsored Research
- Co-Development