Sandy Brownlee University of Stirling. 2 Outline The ground movement problem Real world data sets...
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Sandy Brownlee University of Stirling. 2 Outline The ground movement problem Real world data sets OSM / NATS layouts FR24 movements Handling Uncertainty
2 Outline The ground movement problem Real world data sets OSM
/ NATS layouts FR24 movements Handling Uncertainty Sources of
uncertainty Taxi time uncertainty Possible approaches
Slide 3
3 Ground movement Moving an aircraft from one point to another,
in as quick and fuel-efficient way as possible, considering
existing aircraft movements and route restrictions
Slide 4
4 Data sets Required? Edge+node coordinates, aircraft timings
No substitute for good quality data direct from airports, but
freely available: potential for benchmarking OSM free to copy,
distribute, transmit and adapt our data, as long as you credit
OpenStreetMap and its contributors so the following are
OpenStreetMap contributors Not perfect but surprisingly accurate,
and can be edited to fix imperfections Export XML file taxiways and
runways identified by type tags
Slide 5
5 OSM - Manchester
Slide 6
6 OSM - Stansted
Slide 7
7 NATS AIS (Aeronautical information service) Charts and data
for UK airports Includes coordinates of stands often missing from
OSM
Slide 8
8 Ground movement layouts Used these sources to generate
layouts for Birmingham, Edinburgh, Glasgow, Manchester, Stansted
happy to share if they are of use
Slide 9
9 FlightRadar 24 Real-time tracking of ADS-B transponder data
Lat/lon/altitude every few seconds Works for most airports in
Europe + USA, plus many elsewhere Only includes approx. 50-60% of
flights Somewhat noisy, needs cleaned Cant be used for example
problems, but suitable for analysis of real-world movements Already
used in a handful of publications
Slide 10
10 FR24
Slide 11
11 FR24 actual movements
Slide 12
12 FR24 actual movements
Slide 13
13 FR24 stand use rates
Slide 14
14 Approaches to GM Mixture of routing and scheduling Numerous
approaches tried, using either fixed routes or shortest paths:
Mixed integer linear programming Genetic algorithm Current work is
with QPPTW Based on Dijkstras algorithm find quickest path, while
respecting times reserved for other aircraft Assumes that times and
taxi speed estimates are correct
Slide 15
15 QPPTW at Manchester Demo video
Slide 16
16 Uncertainty Sources of uncertainty Off-block & pushback
times Runway times Taxi speeds Runway crossings Breakdowns /
blockages Weather Others Most tend to be handled by simply running
the route allocation regularly with most up-to-date data
Slide 17
17 Taxi time uncertainty Taxi times for individual edges are
quite variable:
Slide 18
18 Taxi time uncertainty Earlier work (Ravizza 2013, 2014)
found taxi time estimates at Zurich to be: Can just add padding but
this has an impact on taxi times too (preliminary results follow)
Also variation can be cumulative Estimation accuracy % of
movements% of mean taxi time of 443.5s Within 1 min63%13% Within 2
min89%27% Within 3 min97%40%
Slide 19
19 Effect of adding padding Total delay (211 aircraft)
Slide 20
20 Effect of adding padding
Slide 21
21 Handling uncertainty: to-do Improve the modelling further
Informed by more real world data Adding buffers to taxi times
Better understand the trade-off between buffer size and impact on
taxiway capacity A smarter approach to buffering, respecting the
distribution of possible taxi times Adopting methods used in
job-shop scheduling with uncertain processing times (equating
processing time to edge traversal time)
Slide 22
22 Summary Sources for freely-available data: Open street map
NATS AIS FlightRadar24 Handling uncertainty in ground movement