Untangling airports using open source tools on Microsoft Azure

Untangling airports using open source tools on Microsoft Azure


Manchester Airport is the third largest airport
in the UK we have two runways, three terminals handling over twenty-three million passengers
a year So there could be a variety of issues that
we deal with. So I got involved with the Airport Optimization
Project through the Engineering and Physical Sciences
Research Council Sandpit We talked about the issues that we have and we set them to task of coming up with
identifying solutions. Taxiing is important because it links together
the other problems at an airport so you have the order in which aircraft take
off and land you have where they go at the terminal building,
the gates and taxiing links these two things together. The ground movement is particularly important because if you can get a better prediction
of taxi times you can better predict when aircraft are going
to arrive at the runway. Because of that you can actually hold them
on stands for a bit longer If you can hold them on stands for a bit longer,
you can start their engines a bit later Which actually gets you much better pollution
benefits as well as financial cost benefits, because you burn less fuel. We need to try and solve the problem using
optimization techniques. Understandably airports are reluctant to share
their data with just anybody I started looking for ways to try and get
data about taxiing at a whole bunch of different airports with different
characteristics around the world. One of the things cloud computing does is
it brings the power and the data processing ability of huge machines to any researchers desk. I’m really positive about Azure, I think it’s
a great platform. What it gave me access to was a scalable,
powerful machine that I could use to do the job that I wanted. Part of this project, I have written three
open source tools available on GitHub and they are SnapTracks, TaxiGen and GM2KML and these all take care of different aspects
of processing data connected with airport ground movement or taxiing. I was pleantly surprised by how simple it
was to use open source tools with Azure Linux, OpenJDK, SSH all worked just straight away very easily. The computing power that we’ve got now has
allowed us to analyze data in different ways and to pull out different
pieces of information. So we can actually understand a lot better
the true uncertainty in taxiing so we can understand which aircraft take a
long time to get there, which aircraft get there very quickly and we can understand under what circumstances
this is happening, which means we can optimize the system as a whole. As passengers we all have experienced various
delays at airports and if those delays can be reduced by more
efficient operations obviously that benefits us all. Azure really made this research possible in
a reasonable time frame. I had quite a big amount of data that I wanted
to be able to get through and I was able to process it in something
like a tenth of the time I would have done on my desktop So rather than spending several months waiting
for my data to be ready, I had it within a couple weeks. Aviation is an industry that’s growing, so
there are lots of ways that the industry is trying to tackle the impact that growth
could bring to the climate. So the Airport Optimization Project feeds
into that because we want to reduce the amount of CO2 that’s generated when an aircraft lands, taxis and ultimately
departs from the airport. The Airport Optimization Project is going
to be really important to make sure that we maintain the efficiency,
safety and passenger experience.

Leave a Reply

Your email address will not be published. Required fields are marked *