Abstract :
Summary form only given. Recent machine learning techniques are now enabling significant advances in the performance of air transportation decision support systems. This talk will review three vignettes from recent data-driven prototype system development: exploiting radar data and modeling airspace traffic encounters to build a more effective collision avoidance system, extracting information from surface surveillance data to improve airport operations, and learning from operational experience to enhance the Route Availability Planning Tool and facilitate departure management in the vicinity of convective weather. In each case, examples and challenges of data collection, processing, and translation into models and ultimately operational prototype systems will be discussed.
