Regarding feedback: I'm all for increased frequency. As you know, Airline Operations is what I do for a a living, and this is the only place I get data-heavy info like this
I think you may need to broaden your aperature on delay and methods of traffic management? Delay in-and-of itself is not necessarily the root of inefficiency.
There are many sub-metrics to GDP use that, (while not in the public domain), are useful in determining the planning and execution aspects of a GDP.
A short list includes:
- Was the GDP implimented within 2 hours of "start-time"
- Did a Ground Stop precede the GDP
- Did the FAA use a variable AAR, or was the assumption that all hours have equal capacity
- Was the GDP rate modified after implimentation
- Was the GDP cancelled early
Delay should be used as a tool to an acceptable outcome when capacity is constrained. GDPs can be effective when used properly. However, they have to be viewed in the context of other impacts such as completion rate, airborne holding, ground stops, diversions, etc, etc.
Appreciate the thorough feedback. Agree we need to faithfully reproduce the many ways delays are distributed. We've primarily focused on modeling GDPs up to this point because it's doable in Excel; as our modeling becomes more sophisticated (... and built/deployed outside of Excel), we should be able to broaden our aperture (nice metaphor!).
We're also starting to ingest/store some SWIM data: we think it should at least partially fill-in some of gaps in public data.
Working on the cloud infrastructure for our ML model at the moment; then will have to build a front-end. But keep an eye out for something this fall (will certainly use Substack to share its launch)!
Regarding feedback: I'm all for increased frequency. As you know, Airline Operations is what I do for a a living, and this is the only place I get data-heavy info like this
Tim,
I think you may need to broaden your aperature on delay and methods of traffic management? Delay in-and-of itself is not necessarily the root of inefficiency.
There are many sub-metrics to GDP use that, (while not in the public domain), are useful in determining the planning and execution aspects of a GDP.
A short list includes:
- Was the GDP implimented within 2 hours of "start-time"
- Did a Ground Stop precede the GDP
- Did the FAA use a variable AAR, or was the assumption that all hours have equal capacity
- Was the GDP rate modified after implimentation
- Was the GDP cancelled early
Delay should be used as a tool to an acceptable outcome when capacity is constrained. GDPs can be effective when used properly. However, they have to be viewed in the context of other impacts such as completion rate, airborne holding, ground stops, diversions, etc, etc.
Just my opinion.....
Hi Sky,
Appreciate the thorough feedback. Agree we need to faithfully reproduce the many ways delays are distributed. We've primarily focused on modeling GDPs up to this point because it's doable in Excel; as our modeling becomes more sophisticated (... and built/deployed outside of Excel), we should be able to broaden our aperture (nice metaphor!).
We're also starting to ingest/store some SWIM data: we think it should at least partially fill-in some of gaps in public data.
Thanks again (and keep your opinions coming...),
Tim
Is there an APP for Aerology??
Working on the cloud infrastructure for our ML model at the moment; then will have to build a front-end. But keep an eye out for something this fall (will certainly use Substack to share its launch)!