This is quite exciting. In my decades as chief ATC Coordinator for United Airlines I consistently argued that the excuse of “unnecessary and unrecoverable” delay used to wait out a TMI decision point was a mistake. Of course this argument was usually espoused by carriers who would take little of the delay if we lost the wager. I’m also interested in whether your model uses ITWS type of data to forecast the impact of low level winds on approach that can cause compression on final. And lastly, it might be interesting to infer quality performance patterns by ATC as related to controllers, front line managers, and TMU specialists work schedules.
Introduce? Unveil? Announce? Anyways, we've got our first deep learning model to share
This is quite exciting. In my decades as chief ATC Coordinator for United Airlines I consistently argued that the excuse of “unnecessary and unrecoverable” delay used to wait out a TMI decision point was a mistake. Of course this argument was usually espoused by carriers who would take little of the delay if we lost the wager. I’m also interested in whether your model uses ITWS type of data to forecast the impact of low level winds on approach that can cause compression on final. And lastly, it might be interesting to infer quality performance patterns by ATC as related to controllers, front line managers, and TMU specialists work schedules.