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ICRAT Budapest, Hungary June, 2010. Throughput/Complexity Tradeoffs for Routing Traffic in the Presence of Dynamic Weather. Presented by: Valentin Polishchuk, Ph.D. Team of Collaborators. Jimmy Krozel, Ph.D., Metron Aviation, Inc., USA
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ICRATBudapest, HungaryJune, 2010 Throughput/Complexity Tradeoffs for Routing Traffic in the Presence of Dynamic Weather Presented by: Valentin Polishchuk, Ph.D.
Team of Collaborators • Jimmy Krozel, Ph.D., Metron Aviation, Inc., USA • Joseph S.B. Mitchell, Ph.D., Applied Math, Stony Brook University, USA • Valentin Polishchuk, Ph.D., and Anne Pääkkö, Computer Science, University of Helsinki, Finland • Funding provided by: Academy of Finland, NASA and NSF ICRAT ’10 Budapest, Hungary
Algorithmic Problem • Given weather-impacted airspace • Find weather-avoiding trajectories for aircraft • Assumptions en-route fixed flight level (2D, xy) generally unidirectional (e.g., East-to-West) flow ICRAT ’10 Budapest, Hungary
Airspace Sector ICRAT ’10 Budapest, Hungary
Airspace Center ICRAT ’10 Budapest, Hungary
Airspace FCA FCA ICRAT ’10 Budapest, Hungary
Generic Model • Polygonal domain • outer boundary • source and sink edges • obstacles • weather, no-fly zones Source Sink ICRAT ’10 Budapest, Hungary
Aircraft: Disk • Radius = RNP = 5nmi ICRAT ’10 Budapest, Hungary
Airlane: “thick path” • Thickness = 2*RNP= 10nmi MIT = 10nmi ICRAT ’10 Budapest, Hungary
Algorithmic Problem • Given weather-impacted airspace • Find weather-avoiding trajectories for aircraft ICRAT ’10 Budapest, Hungary
Model • Given polygonal domain with obstacles, source and sink • Find thick paths pairwise-disjoint avoiding obstacles ICRAT ’10 Budapest, Hungary
Solution: Search Underlying Grid ICRAT ’10 Budapest, Hungary
Hexagonal disk packing in free space ICRAT ’10 Budapest, Hungary
Graph • Nodes: disks • Edges between touching disks • Source, sink • Every node has capacity 1 ICRAT ’10 Budapest, Hungary
Source-Sink Flow • Decomposes into disjoint paths ICRAT ’10 Budapest, Hungary
Source-Sink Flow MaxFlow → Max # of paths MinCost Flow → Shortest paths • Decomposes into disjoint paths • Inflate thepaths ICRAT ’10 Budapest, Hungary
Examples ICRAT ’10 Budapest, Hungary
Additional constraints: Sector boundaries crossing Communication between ATCs ICRAT ’10 Budapest, Hungary
Higher cost for crossing edges in the graph ICRAT ’10 Budapest, Hungary
Conforming flow ICRAT ’10 Budapest, Hungary
Theoretical guarantee: Max # of paths Maximum Flow Rates for Capacity Estimation in Level Flight with Convective Weather Constraints Krozel, Mitchell, P, Prete Air Traffic Control Quarterly 15(3):209-238, 2007 Capacity = length of shortest B-T path in “critical graph” ℓij = floor(dij/w) ICRAT ’10 Budapest, Hungary
Moving obstacles? • Paths become infeasible ICRAT ’10 Budapest, Hungary
FreeFlight ICRAT ’10 Budapest, Hungary
Solution: Search Time-Expanded Grid ICRAT ’10 Budapest, Hungary
Lifting to (x,y,t) ICRAT ’10 Budapest, Hungary
Obstacles ICRAT ’10 Budapest, Hungary
Time Slicing ICRAT ’10 Budapest, Hungary
Disk Packings ICRAT ’10 Budapest, Hungary
Edges ICRAT ’10 Budapest, Hungary
Node Capacity = 1 ICRAT ’10 Budapest, Hungary
Supersource, supersink ICRAT ’10 Budapest, Hungary
Supersource-supersink flow ICRAT ’10 Budapest, Hungary
Examples ICRAT ’10 Budapest, Hungary
Holding ICRAT ’10 Budapest, Hungary
Holding ICRAT ’10 Budapest, Hungary
The two extremes • Static airlanes • coherent traffic • not adjustable to dynamic constraints • Flexible flow corridors • paths, morphing with obstacles motion • keep threading amidst obstacles • FreeFlight • fully dynamic • “ATC nightmare” ICRAT ’10 Budapest, Hungary